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2407.08715
|
Sensor-Aware Classifiers for Energy-Efficient Time Series Applications
on IoT Devices
|
Time-series data processing is an important component of many real-world applications, such as health monitoring, environmental monitoring, and digital agriculture. These applications collect distinct windows of sensor data (e.g., few seconds) and process them to assess the environment. Machine learning (ML) models are being employed in time-series applications due to their generalization abilities for classification. State-of-the-art time-series applications wait for entire sensor data window to become available before processing the data using ML algorithms, resulting in high sensor energy consumption. However, not all situations require processing full sensor window to make accurate inference. For instance, in activity recognition, sitting and standing activities can be inferred with partial windows. Using this insight, we propose to employ early exit classifiers with partial sensor windows to minimize energy consumption while maintaining accuracy. Specifically, we first utilize multiple early exits with successively increasing amount of data as they become available in a window. If early exits provide inference with high confidence, we return the label and enter low power mode for sensors. The proposed approach has potential to enable significant energy savings in time series applications. We utilize neural networks and random forest classifiers to evaluate our approach. Our evaluations with six datasets show that the proposed approach enables up to 50-60% energy savings on average without any impact on accuracy. The energy savings can enable time-series applications in remote locations with limited energy availability.
|
http://arxiv.org/pdf/2407.08715v1
|
[
"Dina Hussein",
"Lubah Nelson",
"Ganapati Bhat"
] |
2024-07-11T17:50:31Z
|
2024-07-11T17:50:31Z
|
2407.08707
|
Extracting Training Data from Document-Based VQA Models
|
Vision-Language Models (VLMs) have made remarkable progress in document-based Visual Question Answering (i.e., responding to queries about the contents of an input document provided as an image). In this work, we show these models can memorize responses for training samples and regurgitate them even when the relevant visual information has been removed. This includes Personal Identifiable Information (PII) repeated once in the training set, indicating these models could divulge memorised sensitive information and therefore pose a privacy risk. We quantitatively measure the extractability of information in controlled experiments and differentiate between cases where it arises from generalization capabilities or from memorization. We further investigate the factors that influence memorization across multiple state-of-the-art models and propose an effective heuristic countermeasure that empirically prevents the extractability of PII.
|
http://arxiv.org/pdf/2407.08707v1
|
[
"Francesco Pinto",
"Nathalie Rauschmayr",
"Florian Tramèr",
"Philip Torr",
"Federico Tombari"
] |
2024-07-11T17:44:41Z
|
2024-07-11T17:44:41Z
|
2407.08704
|
Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI
Hardware
|
This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic vision sensors. We construct and train hybrid models, blending spiking neural networks (SNNs) and artificial neural networks (ANNs) using PyTorch and Lava frameworks. Our hybrid architecture integrates an SNN for temporal feature extraction and an ANN for classification. We delve into the challenges of deploying such hybrid structures on hardware. Specifically, we deploy individual components on Intel's Neuromorphic Processor Loihi (for SNN) and Jetson Nano (for ANN). We also propose an accumulator circuit to transfer data from the spiking to the non-spiking domain. Furthermore, we conduct comprehensive performance analyses of hybrid SNN-ANN models on a heterogeneous system of neuromorphic and edge AI hardware, evaluating accuracy, latency, power, and energy consumption. Our findings demonstrate that the hybrid spiking networks surpass the baseline ANN model across all metrics and outperform the baseline SNN model in accuracy and latency.
|
http://arxiv.org/pdf/2407.08704v1
|
[
"James Seekings",
"Peyton Chandarana",
"Mahsa Ardakani",
"MohammadReza Mohammadi",
"Ramtin Zand"
] |
2024-07-11T17:40:39Z
|
2024-07-11T17:40:39Z
|
2407.08700
|
Flex-TPU: A Flexible TPU with Runtime Reconfigurable Dataflow
Architecture
|
Tensor processing units (TPUs) are one of the most well-known machine learning (ML) accelerators utilized at large scale in data centers as well as in tiny ML applications. TPUs offer several improvements and advantages over conventional ML accelerators, like graphical processing units (GPUs), being designed specifically to perform the multiply-accumulate (MAC) operations required in the matrix-matrix and matrix-vector multiplies extensively present throughout the execution of deep neural networks (DNNs). Such improvements include maximizing data reuse and minimizing data transfer by leveraging the temporal dataflow paradigms provided by the systolic array architecture. While this design provides a significant performance benefit, the current implementations are restricted to a single dataflow consisting of either input, output, or weight stationary architectures. This can limit the achievable performance of DNN inference and reduce the utilization of compute units. Therefore, the work herein consists of developing a reconfigurable dataflow TPU, called the Flex-TPU, which can dynamically change the dataflow per layer during run-time. Our experiments thoroughly test the viability of the Flex-TPU comparing it to conventional TPU designs across multiple well-known ML workloads. The results show that our Flex-TPU design achieves a significant performance increase of up to 2.75x compared to conventional TPU, with only minor area and power overheads.
|
http://arxiv.org/pdf/2407.08700v1
|
[
"Mohammed Elbtity",
"Peyton Chandarana",
"Ramtin Zand"
] |
2024-07-11T17:33:38Z
|
2024-07-11T17:33:38Z
|
2407.08699
|
Mitigating Catastrophic Forgetting in Language Transfer via Model
Merging
|
As open-weight large language models (LLMs) achieve ever more impressive performances across a wide range of tasks in English, practitioners aim to adapt these models to different languages. However, such language adaptation is often accompanied by catastrophic forgetting of the base model's capabilities, severely limiting the usefulness of the resulting model. We address this issue by proposing Branch-and-Merge (BaM), a new adaptation method based on iteratively merging multiple models, fine-tuned on a subset of the available training data. BaM is based on the insight that this yields lower magnitude but higher quality weight changes, reducing forgetting of the source domain while maintaining learning on the target domain. We demonstrate in an extensive empirical study on Bulgarian and German that BaM can significantly reduce forgetting while matching or even improving target domain performance compared to both standard continued pretraining and instruction finetuning across different model architectures.
|
http://arxiv.org/pdf/2407.08699v1
|
[
"Anton Alexandrov",
"Veselin Raychev",
"Mark Niklas Müller",
"Ce Zhang",
"Martin Vechev",
"Kristina Toutanova"
] |
2024-07-11T17:32:40Z
|
2024-07-11T17:32:40Z
|
2407.08694
|
Cloud Atlas: Efficient Fault Localization for Cloud Systems using
Language Models and Causal Insight
|
Runtime failure and performance degradation is commonplace in modern cloud systems. For cloud providers, automatically determining the root cause of incidents is paramount to ensuring high reliability and availability as prompt fault localization can enable faster diagnosis and triage for timely resolution. A compelling solution explored in recent work is causal reasoning using causal graphs to capture relationships between varied cloud system performance metrics. To be effective, however, systems developers must correctly define the causal graph of their system, which is a time-consuming, brittle, and challenging task that increases in difficulty for large and dynamic systems and requires domain expertise. Alternatively, automated data-driven approaches have limited efficacy for cloud systems due to the inherent rarity of incidents. In this work, we present Atlas, a novel approach to automatically synthesizing causal graphs for cloud systems. Atlas leverages large language models (LLMs) to generate causal graphs using system documentation, telemetry, and deployment feedback. Atlas is complementary to data-driven causal discovery techniques, and we further enhance Atlas with a data-driven validation step. We evaluate Atlas across a range of fault localization scenarios and demonstrate that Atlas is capable of generating causal graphs in a scalable and generalizable manner, with performance that far surpasses that of data-driven algorithms and is commensurate to the ground-truth baseline.
|
http://arxiv.org/pdf/2407.08694v1
|
[
"Zhiqiang Xie",
"Yujia Zheng",
"Lizi Ottens",
"Kun Zhang",
"Christos Kozyrakis",
"Jonathan Mace"
] |
2024-07-11T17:31:12Z
|
2024-07-11T17:31:12Z
|
2407.08689
|
Operationalizing the Blueprint for an AI Bill of Rights: Recommendations
for Practitioners, Researchers, and Policy Makers
|
As Artificial Intelligence (AI) tools are increasingly employed in diverse real-world applications, there has been significant interest in regulating these tools. To this end, several regulatory frameworks have been introduced by different countries worldwide. For example, the European Union recently passed the AI Act, the White House issued an Executive Order on safe, secure, and trustworthy AI, and the White House Office of Science and Technology Policy issued the Blueprint for an AI Bill of Rights (AI BoR). Many of these frameworks emphasize the need for auditing and improving the trustworthiness of AI tools, underscoring the importance of safety, privacy, explainability, fairness, and human fallback options. Although these regulatory frameworks highlight the necessity of enforcement, practitioners often lack detailed guidance on implementing them. Furthermore, the extensive research on operationalizing each of these aspects is frequently buried in technical papers that are difficult for practitioners to parse. In this write-up, we address this shortcoming by providing an accessible overview of existing literature related to operationalizing regulatory principles. We provide easy-to-understand summaries of state-of-the-art literature and highlight various gaps that exist between regulatory guidelines and existing AI research, including the trade-offs that emerge during operationalization. We hope that this work not only serves as a starting point for practitioners interested in learning more about operationalizing the regulatory guidelines outlined in the Blueprint for an AI BoR but also provides researchers with a list of critical open problems and gaps between regulations and state-of-the-art AI research. Finally, we note that this is a working paper and we invite feedback in line with the purpose of this document as described in the introduction.
|
http://arxiv.org/pdf/2407.08689v1
|
[
"Alex Oesterling",
"Usha Bhalla",
"Suresh Venkatasubramanian",
"Himabindu Lakkaraju"
] |
2024-07-11T17:28:07Z
|
2024-07-11T17:28:07Z
|
2403.17933
|
SLEDGE: Synthesizing Driving Environments with Generative Models and
Rule-Based Traffic
|
SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs. Its core component is a learned model that is able to generate agent bounding boxes and lane graphs. The model's outputs serve as an initial state for rule-based traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as their connectivity and variable count per scene, render the naive application of most modern generative models to this task non-trivial. Therefore, together with a systematic study of existing lane graph representations, we introduce a novel raster-to-vector autoencoder. It encodes agents and the lane graph into distinct channels in a rasterized latent map. This facilitates both lane-conditioned agent generation and combined generation of lanes and agents with a Diffusion Transformer. Using generated entities in SLEDGE enables greater control over the simulation, e.g. upsampling turns or increasing traffic density. Further, SLEDGE can support 500m long routes, a capability not found in existing data-driven simulators like nuPlan. It presents new challenges for planning algorithms, evidenced by failure rates of over 40% for PDM, the winner of the 2023 nuPlan challenge, when tested on hard routes and dense traffic generated by our model. Compared to nuPlan, SLEDGE requires 500$times$ less storage to set up (<4 GB), making it a more accessible option and helping with democratizing future research in this field.
|
http://arxiv.org/pdf/2403.17933v2
|
[
"Kashyap Chitta",
"Daniel Dauner",
"Andreas Geiger"
] |
2024-07-11T17:27:49Z
|
2024-03-26T17:58:29Z
|
2401.00003
|
Generative Inverse Design of Metamaterials with Functional Responses by
Interpretable Learning
|
Metamaterials with functional responses, such as wave-based responses or deformation-induced property variation under external stimuli, can exhibit varying properties or functionalities under different conditions. Herein, we aim at rapid inverse design of these metamaterials to meet target qualitative functional behaviors. This inverse problem is challenging due to its intractability and the existence of non-unique solutions. Past works mainly focus on deep-learning-based methods that are data-demanding, require time-consuming training and hyperparameter tuning, and are non-interpretable. To overcome these limitations, we propose the Random-forest-based Interpretable Generative Inverse Design (RIGID), an iteration-free, single-shot inverse design method to achieve the fast generation of metamaterial designs with on-demand functional behaviors. Unlike most existing methods, by exploiting the interpretability of the random forest, we eliminate the need to train an inverse model mapping responses to designs. Based on the likelihood of target satisfaction derived from the trained forward model, one can sample design solutions using Markov chain Monte Carlo methods. The RIGID method therefore functions as a generative model that captures the conditional distribution of satisfying solutions given a design target. We demonstrate the effectiveness and efficiency of RIGID on both acoustic and optical metamaterial design problems where only small datasets (less than 250 training samples) are available. Synthetic design problems are created to further illustrate and validate the mechanism of likelihood estimation in RIGID. This work offers a new perspective on solving on-demand inverse design problems, showcasing the potential for incorporating interpretable machine learning into generative design and eliminating its large data requirement.
|
http://arxiv.org/pdf/2401.00003v2
|
[
"Wei \"Wayne\" Chen",
"Rachel Sun",
"Doksoo Lee",
"Carlos M. Portela",
"Wei Chen"
] |
2024-07-11T17:27:35Z
|
2023-12-08T04:24:03Z
|
2407.08681
|
Hardware Neural Control of CartPole and F1TENTH Race Car
|
Nonlinear model predictive control (NMPC) has proven to be an effective control method, but it is expensive to compute. This work demonstrates the use of hardware FPGA neural network controllers trained to imitate NMPC with supervised learning. We use these Neural Controllers (NCs) implemented on inexpensive embedded FPGA hardware for high frequency control on physical cartpole and F1TENTH race car. Our results show that the NCs match the control performance of the NMPCs in simulation and outperform it in reality, due to the faster control rate that is afforded by the quick FPGA NC inference. We demonstrate kHz control rates for a physical cartpole and offloading control to the FPGA hardware on the F1TENTH car. Code and hardware implementation for this paper are available at https:// github.com/SensorsINI/Neural-Control-Tools.
|
http://arxiv.org/pdf/2407.08681v1
|
[
"Marcin Paluch",
"Florian Bolli",
"Xiang Deng",
"Antonio Rios Navarro",
"Chang Gao",
"Tobi Delbruck"
] |
2024-07-11T17:14:19Z
|
2024-07-11T17:14:19Z
|
2407.08678
|
How to beat a Bayesian adversary
|
Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model's prediction through a small, directed perturbation of the model's input - an issue in safety-critical applications. Adversarially robust machine learning is usually based on a minmax optimisation problem that minimises the machine learning loss under maximisation-based adversarial attacks. In this work, we study adversaries that determine their attack using a Bayesian statistical approach rather than maximisation. The resulting Bayesian adversarial robustness problem is a relaxation of the usual minmax problem. To solve this problem, we propose Abram - a continuous-time particle system that shall approximate the gradient flow corresponding to the underlying learning problem. We show that Abram approximates a McKean-Vlasov process and justify the use of Abram by giving assumptions under which the McKean-Vlasov process finds the minimiser of the Bayesian adversarial robustness problem. We discuss two ways to discretise Abram and show its suitability in benchmark adversarial deep learning experiments.
|
http://arxiv.org/pdf/2407.08678v1
|
[
"Zihan Ding",
"Kexin Jin",
"Jonas Latz",
"Chenguang Liu"
] |
2024-07-11T17:12:42Z
|
2024-07-11T17:12:42Z
|
2406.13283
|
Large-Scale Dataset Pruning in Adversarial Training through Data
Importance Extrapolation
|
Their vulnerability to small, imperceptible attacks limits the adoption of deep learning models to real-world systems. Adversarial training has proven to be one of the most promising strategies against these attacks, at the expense of a substantial increase in training time. With the ongoing trend of integrating large-scale synthetic data this is only expected to increase even further. Thus, the need for data-centric approaches that reduce the number of training samples while maintaining accuracy and robustness arises. While data pruning and active learning are prominent research topics in deep learning, they are as of now largely unexplored in the adversarial training literature. We address this gap and propose a new data pruning strategy based on extrapolating data importance scores from a small set of data to a larger set. In an empirical evaluation, we demonstrate that extrapolation-based pruning can efficiently reduce dataset size while maintaining robustness.
|
http://arxiv.org/pdf/2406.13283v2
|
[
"Björn Nieth",
"Thomas Altstidl",
"Leo Schwinn",
"Björn Eskofier"
] |
2024-07-11T17:10:24Z
|
2024-06-19T07:23:51Z
|
2407.08668
|
Estimation of spatio-temporal extremes via generative neural networks
|
Recent methods in modeling spatial extreme events have focused on utilizing parametric max-stable processes and their underlying dependence structure. In this work, we provide a unified approach for analyzing spatial extremes with little available data by estimating the distribution of model parameters or the spatial dependence directly. By employing recent developments in generative neural networks we predict a full sample-based distribution, allowing for direct assessment of uncertainty regarding model parameters or other parameter dependent functionals. We validate our method by fitting several simulated max-stable processes, showing a high accuracy of the approach, regarding parameter estimation, as well as uncertainty quantification. Additional robustness checks highlight the generalization and extrapolation capabilities of the model, while an application to precipitation extremes across Western Germany demonstrates the usability of our approach in real-world scenarios.
|
http://arxiv.org/pdf/2407.08668v1
|
[
"Christopher Bülte",
"Lisa Leimenstoll",
"Melanie Schienle"
] |
2024-07-11T16:57:17Z
|
2024-07-11T16:57:17Z
|
2402.14194
|
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human
Racing Gameplay
|
Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must model complex environment dynamics and human decision-making. Sequence modeling is highly effective in capturing intricate patterns of motion sequences but struggles to adapt to new environments or distribution shifts that are common in real-world robotics tasks. In contrast, Adversarial Imitation Learning (AIL) can mitigate this effect, but struggles with sample inefficiency and handling complex motion patterns. Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL. BeTAIL adds an AIL residual policy to the BeT policy to model the sequential decision-making process of human experts and correct for out-of-distribution states or shifts in environment dynamics. We test BeTAIL on three challenges with expert-level demonstrations of real human gameplay in Gran Turismo Sport. Our proposed residual BeTAIL reduces environment interactions and improves racing performance and stability, even when the BeT is pretrained on different tracks than downstream learning. Videos and code available at: https://sites.google.com/berkeley.edu/BeTAIL/home.
|
http://arxiv.org/pdf/2402.14194v2
|
[
"Catherine Weaver",
"Chen Tang",
"Ce Hao",
"Kenta Kawamoto",
"Masayoshi Tomizuka",
"Wei Zhan"
] |
2024-07-11T16:50:08Z
|
2024-02-22T00:38:43Z
|
2407.08659
|
Controlling the Fidelity and Diversity of Deep Generative Models via
Pseudo Density
|
We introduce an approach to bias deep generative models, such as GANs and diffusion models, towards generating data with either enhanced fidelity or increased diversity. Our approach involves manipulating the distribution of training and generated data through a novel metric for individual samples, named pseudo density, which is based on the nearest-neighbor information from real samples. Our approach offers three distinct techniques to adjust the fidelity and diversity of deep generative models: 1) Per-sample perturbation, enabling precise adjustments for individual samples towards either more common or more unique characteristics; 2) Importance sampling during model inference to enhance either fidelity or diversity in the generated data; 3) Fine-tuning with importance sampling, which guides the generative model to learn an adjusted distribution, thus controlling fidelity and diversity. Furthermore, our fine-tuning method demonstrates the ability to improve the Frechet Inception Distance (FID) for pre-trained generative models with minimal iterations.
|
http://arxiv.org/pdf/2407.08659v1
|
[
"Shuangqi Li",
"Chen Liu",
"Tong Zhang",
"Hieu Le",
"Sabine Süsstrunk",
"Mathieu Salzmann"
] |
2024-07-11T16:46:04Z
|
2024-07-11T16:46:04Z
|
2312.05073
|
A Distributed ADMM-based Deep Learning Approach for Thermal Control in
Multi-Zone Buildings under Demand Response Events
|
The increasing electricity use and reliance on intermittent renewable energy sources challenge power grid management during peak demand, making Demand Response programs and energy conservation measures essential. This research combines distributed optimization using ADMM with deep learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. While most algorithms are either centralized or require prior knowledge about the building's structure, our approach is distributed and fully data-driven. The proposed algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.
|
http://arxiv.org/pdf/2312.05073v2
|
[
"Vincent Taboga",
"Hanane Dagdougui"
] |
2024-07-11T16:43:38Z
|
2023-12-08T14:46:50Z
|
2407.08655
|
SPOCKMIP: Segmentation of Vessels in MRAs with Enhanced Continuity using
Maximum Intensity Projection as Loss
|
Identification of vessel structures of different sizes in biomedical images is crucial in the diagnosis of many neurodegenerative diseases. However, the sparsity of good-quality annotations of such images makes the task of vessel segmentation challenging. Deep learning offers an efficient way to segment vessels of different sizes by learning their high-level feature representations and the spatial continuity of such features across dimensions. Semi-supervised patch-based approaches have been effective in identifying small vessels of one to two voxels in diameter. This study focuses on improving the segmentation quality by considering the spatial correlation of the features using the Maximum Intensity Projection~(MIP) as an additional loss criterion. Two methods are proposed with the incorporation of MIPs of label segmentation on the single~(z-axis) and multiple perceivable axes of the 3D volume. The proposed MIP-based methods produce segmentations with improved vessel continuity, which is evident in visual examinations of ROIs. Patch-based training is improved by introducing an additional loss term, MIP loss, to penalise the predicted discontinuity of vessels. A training set of 14 volumes is selected from the StudyForrest dataset comprising of 18 7-Tesla 3D Time-of-Flight~(ToF) Magnetic Resonance Angiography (MRA) images. The generalisation performance of the method is evaluated using the other unseen volumes in the dataset. It is observed that the proposed method with multi-axes MIP loss produces better quality segmentations with a median Dice of $80.245 pm 0.129$. Also, the method with single-axis MIP loss produces segmentations with a median Dice of $79.749 pm 0.109$. Furthermore, a visual comparison of the ROIs in the predicted segmentation reveals a significant improvement in the continuity of the vessels when MIP loss is incorporated into training.
|
http://arxiv.org/pdf/2407.08655v1
|
[
"Chethan Radhakrishna",
"Karthikesh Varma Chintalapati",
"Sri Chandana Hudukula Ram Kumar",
"Raviteja Sutrave",
"Hendrik Mattern",
"Oliver Speck",
"Andreas Nürnberger",
"Soumick Chatterjee"
] |
2024-07-11T16:39:24Z
|
2024-07-11T16:39:24Z
|
2407.09573
|
Have We Reached AGI? Comparing ChatGPT, Claude, and Gemini to Human
Literacy and Education Benchmarks
|
Recent advancements in AI, particularly in large language models (LLMs) like ChatGPT, Claude, and Gemini, have prompted questions about their proximity to Artificial General Intelligence (AGI). This study compares LLM performance on educational benchmarks with Americans' average educational attainment and literacy levels, using data from the U.S. Census Bureau and technical reports. Results show that LLMs significantly outperform human benchmarks in tasks such as undergraduate knowledge and advanced reading comprehension, indicating substantial progress toward AGI. However, true AGI requires broader cognitive assessments. The study highlights the implications for AI development, education, and societal impact, emphasizing the need for ongoing research and ethical considerations.
|
http://arxiv.org/pdf/2407.09573v1
|
[
"Mfon Akpan"
] |
2024-07-11T16:38:40Z
|
2024-07-11T16:38:40Z
|
2407.08654
|
Adaptive Smooth Non-Stationary Bandits
|
We study a $K$-armed non-stationary bandit model where rewards change smoothly, as captured by H"{o}lder class assumptions on rewards as functions of time. Such smooth changes are parametrized by a H"{o}lder exponent $beta$ and coefficient $lambda$. While various sub-cases of this general model have been studied in isolation, we first establish the minimax dynamic regret rate generally for all $K,beta,lambda$. Next, we show this optimal dynamic regret can be attained adaptively, without knowledge of $beta,lambda$. To contrast, even with parameter knowledge, upper bounds were only previously known for limited regimes $betaleq 1$ and $beta=2$ (Slivkins, 2014; Krishnamurthy and Gopalan, 2021; Manegueu et al., 2021; Jia et al.,2023). Thus, our work resolves open questions raised by these disparate threads of the literature. We also study the problem of attaining faster gap-dependent regret rates in non-stationary bandits. While such rates are long known to be impossible in general (Garivier and Moulines, 2011), we show that environments admitting a safe arm (Suk and Kpotufe, 2022) allow for much faster rates than the worst-case scaling with $sqrt{T}$. While previous works in this direction focused on attaining the usual logarithmic regret bounds, as summed over stationary periods, our new gap-dependent rates reveal new optimistic regimes of non-stationarity where even the logarithmic bounds are pessimistic. We show our new gap-dependent rate is tight and that its achievability (i.e., as made possible by a safe arm) has a surprisingly simple and clean characterization within the smooth H"{o}lder class model.
|
http://arxiv.org/pdf/2407.08654v1
|
[
"Joe Suk"
] |
2024-07-11T16:37:15Z
|
2024-07-11T16:37:15Z
|
2311.11413
|
Large Pre-trained time series models for cross-domain Time series
analysis tasks
|
Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a foundational time-series model from multi-domain time-series datasets: extracting semantically useful tokenized inputs to the model across heterogenous time-series from different domains. We propose Large Pre-trained Time-series Models (LPTM) that introduces a novel method of textit{adaptive segmentation} that automatically identifies optimal dataset-specific segmentation strategy during pre-training. This enables LPTM to perform similar to or better than domain-specific state-of-art model when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings. LPTM achieves superior forecasting and time-series classification results taking up to 40% less data and 50% less training time compared to state-of-art baselines.
|
http://arxiv.org/pdf/2311.11413v2
|
[
"Harshavardhan Kamarthi",
"B. Aditya Prakash"
] |
2024-07-11T16:32:12Z
|
2023-11-19T20:16:16Z
|
2407.08649
|
Confidence-based Estimators for Predictive Performance in Model
Monitoring
|
After a machine learning model has been deployed into production, its predictive performance needs to be monitored. Ideally, such monitoring can be carried out by comparing the model's predictions against ground truth labels. For this to be possible, the ground truth labels must be available relatively soon after inference. However, there are many use cases where ground truth labels are available only after a significant delay, or in the worst case, not at all. In such cases, directly monitoring the model's predictive performance is impossible. Recently, novel methods for estimating the predictive performance of a model when ground truth is unavailable have been developed. Many of these methods leverage model confidence or other uncertainty estimates and are experimentally compared against a naive baseline method, namely Average Confidence (AC), which estimates model accuracy as the average of confidence scores for a given set of predictions. However, until now the theoretical properties of the AC method have not been properly explored. In this paper, we try to fill this gap by reviewing the AC method and show that under certain general assumptions, it is an unbiased and consistent estimator of model accuracy with many desirable properties. We also compare this baseline estimator against some more complex estimators empirically and show that in many cases the AC method is able to beat the others, although the comparative quality of the different estimators is heavily case-dependent.
|
http://arxiv.org/pdf/2407.08649v1
|
[
"Juhani Kivimäki",
"Jakub Białek",
"Jukka K. Nurminen",
"Wojtek Kuberski"
] |
2024-07-11T16:28:31Z
|
2024-07-11T16:28:31Z
|
2407.07788
|
BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark
|
We introduce BiGym, a new benchmark and learning environment for mobile bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home environments, ranging from simple target reaching to complex kitchen cleaning. To capture the real-world performance accurately, we provide human-collected demonstrations for each task, reflecting the diverse modalities found in real-world robot trajectories. BiGym supports a variety of observations, including proprioceptive data and visual inputs such as RGB, and depth from 3 camera views. To validate the usability of BiGym, we thoroughly benchmark the state-of-the-art imitation learning algorithms and demo-driven reinforcement learning algorithms within the environment and discuss the future opportunities.
|
http://arxiv.org/pdf/2407.07788v2
|
[
"Nikita Chernyadev",
"Nicholas Backshall",
"Xiao Ma",
"Yunfan Lu",
"Younggyo Seo",
"Stephen James"
] |
2024-07-11T16:26:09Z
|
2024-07-10T16:04:18Z
|
2407.08647
|
From Real to Cloned Singer Identification
|
Cloned voices of popular singers sound increasingly realistic and have gained popularity over the past few years. They however pose a threat to the industry due to personality rights concerns. As such, methods to identify the original singer in synthetic voices are needed. In this paper, we investigate how singer identification methods could be used for such a task. We present three embedding models that are trained using a singer-level contrastive learning scheme, where positive pairs consist of segments with vocals from the same singers. These segments can be mixtures for the first model, vocals for the second, and both for the third. We demonstrate that all three models are highly capable of identifying real singers. However, their performance deteriorates when classifying cloned versions of singers in our evaluation set. This is especially true for models that use mixtures as an input. These findings highlight the need to understand the biases that exist within singer identification systems, and how they can influence the identification of voice deepfakes in music.
|
http://arxiv.org/pdf/2407.08647v1
|
[
"Dorian Desblancs",
"Gabriel Meseguer-Brocal",
"Romain Hennequin",
"Manuel Moussallam"
] |
2024-07-11T16:25:21Z
|
2024-07-11T16:25:21Z
|
2405.05241
|
BenthicNet: A global compilation of seafloor images for deep learning
applications
|
Advances in underwater imaging enable the collection of extensive seafloor image datasets that are necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering expedient mobilization of this crucial environmental information. Recent machine learning approaches provide opportunities to increase the efficiency with which seafloor image datasets are analyzed, yet large and consistent datasets necessary to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 2.6 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for use by the scientific community at https://doi.org/10.20383/103.0614.
|
http://arxiv.org/pdf/2405.05241v2
|
[
"Scott C. Lowe",
"Benjamin Misiuk",
"Isaac Xu",
"Shakhboz Abdulazizov",
"Amit R. Baroi",
"Alex C. Bastos",
"Merlin Best",
"Vicki Ferrini",
"Ariell Friedman",
"Deborah Hart",
"Ove Hoegh-Guldberg",
"Daniel Ierodiaconou",
"Julia Mackin-McLaughlin",
"Kathryn Markey",
"Pedro S. Menandro",
"Jacquomo Monk",
"Shreya Nemani",
"John O'Brien",
"Elizabeth Oh",
"Luba Y. Reshitnyk",
"Katleen Robert",
"Chris M. Roelfsema",
"Jessica A. Sameoto",
"Alexandre C. G. Schimel",
"Jordan A. Thomson",
"Brittany R. Wilson",
"Melisa C. Wong",
"Craig J. Brown",
"Thomas Trappenberg"
] |
2024-07-11T16:24:52Z
|
2024-05-08T17:37:57Z
|
2407.08641
|
How more data can hurt: Instability and regularization in
next-generation reservoir computing
|
It has been found recently that more data can, counter-intuitively, hurt the performance of deep neural networks. Here, we show that a more extreme version of the phenomenon occurs in data-driven models of dynamical systems. To elucidate the underlying mechanism, we focus on next-generation reservoir computing (NGRC) -- a popular framework for learning dynamics from data. We find that, despite learning a better representation of the flow map with more training data, NGRC can adopt an ill-conditioned ``integrator'' and lose stability. We link this data-induced instability to the auxiliary dimensions created by the delayed states in NGRC. Based on these findings, we propose simple strategies to mitigate the instability, either by increasing regularization strength in tandem with data size, or by carefully introducing noise during training. Our results highlight the importance of proper regularization in data-driven modeling of dynamical systems.
|
http://arxiv.org/pdf/2407.08641v1
|
[
"Yuanzhao Zhang",
"Sean P. Cornelius"
] |
2024-07-11T16:22:13Z
|
2024-07-11T16:22:13Z
|
2407.08639
|
$β$-DPO: Direct Preference Optimization with Dynamic $β$
|
Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO is sensitive to the fine-tuning of its trade-off parameter $beta$, as well as to the quality of the preference data. We analyze the impact of $beta$ and data quality on DPO, uncovering that optimal $beta$ values vary with the informativeness of pairwise data. Addressing the limitations of static $beta$ values, we introduce a novel framework that dynamically calibrates $beta$ at the batch level, informed by data quality considerations. Additionally, our method incorporates $beta$-guided data filtering to safeguard against the influence of outliers. Through empirical evaluation, we demonstrate that our dynamic $beta$ adjustment technique significantly improves DPO's performance across a range of models and datasets, offering a more robust and adaptable training paradigm for aligning LLMs with human feedback. The code is available at url{https://github.com/junkangwu/beta-DPO}.
|
http://arxiv.org/pdf/2407.08639v1
|
[
"Junkang Wu",
"Yuexiang Xie",
"Zhengyi Yang",
"Jiancan Wu",
"Jinyang Gao",
"Bolin Ding",
"Xiang Wang",
"Xiangnan He"
] |
2024-07-11T16:21:18Z
|
2024-07-11T16:21:18Z
|
2407.07874
|
Toto: Time Series Optimized Transformer for Observability
|
This technical report describes the Time Series Optimized Transformer for Observability (Toto), a new state of the art foundation model for time series forecasting developed by Datadog. In addition to advancing the state of the art on generalized time series benchmarks in domains such as electricity and weather, this model is the first general-purpose time series forecasting foundation model to be specifically tuned for observability metrics. Toto was trained on a dataset of one trillion time series data points, the largest among all currently published time series foundation models. Alongside publicly available time series datasets, 75% of the data used to train Toto consists of fully anonymous numerical metric data points from the Datadog platform. In our experiments, Toto outperforms existing time series foundation models on observability data. It does this while also excelling at general-purpose forecasting tasks, achieving state-of-the-art zero-shot performance on multiple open benchmark datasets.
|
http://arxiv.org/pdf/2407.07874v2
|
[
"Ben Cohen",
"Emaad Khwaja",
"Kan Wang",
"Charles Masson",
"Elise Ramé",
"Youssef Doubli",
"Othmane Abou-Amal"
] |
2024-07-11T16:18:40Z
|
2024-07-10T17:40:30Z
|
2407.08632
|
Generalization Error Matters in Decentralized Learning Under Byzantine
Attacks
|
Recently, decentralized learning has emerged as a popular peer-to-peer signal and information processing paradigm that enables model training across geographically distributed agents in a scalable manner, without the presence of any central server. When some of the agents are malicious (also termed as Byzantine), resilient decentralized learning algorithms are able to limit the impact of these Byzantine agents without knowing their number and identities, and have guaranteed optimization errors. However, analysis of the generalization errors, which are critical to implementations of the trained models, is still lacking. In this paper, we provide the first analysis of the generalization errors for a class of popular Byzantine-resilient decentralized stochastic gradient descent (DSGD) algorithms. Our theoretical results reveal that the generalization errors cannot be entirely eliminated because of the presence of the Byzantine agents, even if the number of training samples are infinitely large. Numerical experiments are conducted to confirm our theoretical results.
|
http://arxiv.org/pdf/2407.08632v1
|
[
"Haoxiang Ye",
"Qing Ling"
] |
2024-07-11T16:12:53Z
|
2024-07-11T16:12:53Z
|
2407.08626
|
RoboMorph: Evolving Robot Morphology using Large Language Models
|
We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. In this framework, we represent each robot design as a grammar and leverage the capabilities of LLMs to navigate the extensive robot design space, which is traditionally time-consuming and computationally demanding. By integrating automatic prompt design and a reinforcement learning based control algorithm, RoboMorph iteratively improves robot designs through feedback loops. Our experimental results demonstrate that RoboMorph can successfully generate nontrivial robots that are optimized for a single terrain while showcasing improvements in morphology over successive evolutions. Our approach demonstrates the potential of using LLMs for data-driven and modular robot design, providing a promising methodology that can be extended to other domains with similar design frameworks.
|
http://arxiv.org/pdf/2407.08626v1
|
[
"Kevin Qiu",
"Krzysztof Ciebiera",
"Paweł Fijałkowski",
"Marek Cygan",
"Łukasz Kuciński"
] |
2024-07-11T16:05:56Z
|
2024-07-11T16:05:56Z
|
2310.19163
|
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning
with Adversarial Data Manipulation
|
Federated learning has emerged as a promising privacy-preserving solution for machine learning domains that rely on user interactions, particularly recommender systems and online learning to rank. While there has been substantial research on the privacy of traditional federated learning, little attention has been paid to the privacy properties of these interaction-based settings. In this work, we show that users face an elevated risk of having their private interactions reconstructed by the central server when the server can control the training features of the items that users interact with. We introduce RAIFLE, a novel optimization-based attack framework where the server actively manipulates the features of the items presented to users to increase the success rate of reconstruction. Our experiments with federated recommendation and online learning-to-rank scenarios demonstrate that RAIFLE is significantly more powerful than existing reconstruction attacks like gradient inversion, achieving high performance consistently in most settings. We discuss the pros and cons of several possible countermeasures to defend against RAIFLE in the context of interaction-based federated learning. Our code is open-sourced at https://github.com/dzungvpham/raifle.
|
http://arxiv.org/pdf/2310.19163v2
|
[
"Dzung Pham",
"Shreyas Kulkarni",
"Amir Houmansadr"
] |
2024-07-11T16:04:27Z
|
2023-10-29T21:47:24Z
|
2407.08623
|
Surpassing Cosine Similarity for Multidimensional Comparisons: Dimension
Insensitive Euclidean Metric (DIEM)
|
The advancement in computational power and hardware efficiency has enabled the tackling of increasingly complex and high-dimensional problems. While artificial intelligence (AI) has achieved remarkable results in various scientific and technological fields, the interpretability of these high-dimensional solutions remains challenging. A critical issue in this context is the comparison of multidimensional quantities, which is essential in techniques like Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and k-means clustering. Common metrics such as cosine similarity, Euclidean distance, and Manhattan distance are often used for such comparisons - for example in muscular synergies of the human motor control system. However, their applicability and interpretability diminish as dimensionality increases. This paper provides a comprehensive analysis of the effects of dimensionality on these three widely used metrics. Our results reveal significant limitations of cosine similarity, particularly its dependency on the dimensionality of the vectors, leading to biased and less interpretable outcomes. To address this, we introduce the Dimension Insensitive Euclidean Metric (DIEM), derived from the Euclidean distance, which demonstrates superior robustness and generalizability across varying dimensions. DIEM maintains consistent variability and eliminates the biases observed in traditional metrics, making it a more reliable tool for high-dimensional comparisons. This novel metric has the potential to replace cosine similarity, providing a more accurate and insightful method to analyze multidimensional data in fields ranging from neuromotor control to machine learning and deep learning.
|
http://arxiv.org/pdf/2407.08623v1
|
[
"Federico Tessari",
"Neville Hogan"
] |
2024-07-11T16:00:22Z
|
2024-07-11T16:00:22Z
|
2401.07844
|
The ODE Method for Stochastic Approximation and Reinforcement Learning
with Markovian Noise
|
Stochastic approximation is a class of algorithms that update a vector iteratively, incrementally, and stochastically, including, e.g., stochastic gradient descent and temporal difference learning. One fundamental challenge in analyzing a stochastic approximation algorithm is to establish its stability, i.e., to show that the stochastic vector iterates are bounded almost surely. In this paper, we extend the celebrated Borkar-Meyn theorem for stability from the Martingale difference noise setting to the Markovian noise setting, which greatly improves its applicability in reinforcement learning, especially in those off-policy reinforcement learning algorithms with linear function approximation and eligibility traces. Central to our analysis is the diminishing asymptotic rate of change of a few functions, which is implied by both a form of strong law of large numbers and a commonly used V4 Lyapunov drift condition and trivially holds if the Markov chain is finite and irreducible.
|
http://arxiv.org/pdf/2401.07844v5
|
[
"Shuze Liu",
"Shuhang Chen",
"Shangtong Zhang"
] |
2024-07-11T15:51:39Z
|
2024-01-15T17:20:17Z
|
2407.08610
|
Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate
Video-based Bug Reports
|
Video-based bug reports are increasingly being used to document bugs for programs centered around a graphical user interface (GUI). However, developing automated techniques to manage video-based reports is challenging as it requires identifying and understanding often nuanced visual patterns that capture key information about a reported bug. In this paper, we aim to overcome these challenges by advancing the bug report management task of duplicate detection for video-based reports. To this end, we introduce a new approach, called JANUS, that adapts the scene-learning capabilities of vision transformers to capture subtle visual and textual patterns that manifest on app UI screens - which is key to differentiating between similar screens for accurate duplicate report detection. JANUS also makes use of a video alignment technique capable of adaptive weighting of video frames to account for typical bug manifestation patterns. In a comprehensive evaluation on a benchmark containing 7,290 duplicate detection tasks derived from 270 video-based bug reports from 90 Android app bugs, the best configuration of our approach achieves an overall mRR/mAP of 89.8%/84.7%, and for the large majority of duplicate detection tasks, outperforms prior work by around 9% to a statistically significant degree. Finally, we qualitatively illustrate how the scene-learning capabilities provided by Janus benefits its performance.
|
http://arxiv.org/pdf/2407.08610v1
|
[
"Yanfu Yan",
"Nathan Cooper",
"Oscar Chaparro",
"Kevin Moran",
"Denys Poshyvanyk"
] |
2024-07-11T15:48:36Z
|
2024-07-11T15:48:36Z
|
2404.04814
|
Inference-Time Rule Eraser: Fair Recognition via Distilling and Removing
Biased Rules
|
Machine learning models often make predictions based on biased features such as gender, race, and other social attributes, posing significant fairness risks, especially in societal applications, such as hiring, banking, and criminal justice. Traditional approaches to addressing this issue involve retraining or fine-tuning neural networks with fairness-aware optimization objectives. However, these methods can be impractical due to significant computational resources, complex industrial tests, and the associated CO2 footprint. Additionally, regular users often fail to fine-tune models because they lack access to model parameters In this paper, we introduce the Inference-Time Rule Eraser (Eraser), a novel method designed to address fairness concerns by removing biased decision-making rules from deployed models during inference without altering model weights. We begin by establishing a theoretical foundation for modifying model outputs to eliminate biased rules through Bayesian analysis. Next, we present a specific implementation of Eraser that involves two stages: (1) distilling the biased rules from the deployed model into an additional patch model, and (2) removing these biased rules from the output of the deployed model during inference. Extensive experiments validate the effectiveness of our approach, showcasing its superior performance in addressing fairness concerns in AI systems.
|
http://arxiv.org/pdf/2404.04814v3
|
[
"Yi Zhang",
"Dongyuan Lu",
"Jitao Sang"
] |
2024-07-11T15:33:35Z
|
2024-04-07T05:47:41Z
|
2407.04272
|
Accelerating Communication in Deep Learning Recommendation Model
Training with Dual-Level Adaptive Lossy Compression
|
DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitates the use of multiple devices/GPUs for efficient training. A significant bottleneck in this process is the time-consuming all-to-all communication required to collect embedding data from all devices. To mitigate this, we introduce a method that employs error-bounded lossy compression to reduce the communication data size and accelerate DLRM training. We develop a novel error-bounded lossy compression algorithm, informed by an in-depth analysis of embedding data features, to achieve high compression ratios. Moreover, we introduce a dual-level adaptive strategy for error-bound adjustment, spanning both table-wise and iteration-wise aspects, to balance the compression benefits with the potential impacts on accuracy. We further optimize our compressor for PyTorch tensors on GPUs, minimizing compression overhead. Evaluation shows that our method achieves a 1.38$times$ training speedup with a minimal accuracy impact.
|
http://arxiv.org/pdf/2407.04272v3
|
[
"Hao Feng",
"Boyuan Zhang",
"Fanjiang Ye",
"Min Si",
"Ching-Hsiang Chu",
"Jiannan Tian",
"Chunxing Yin",
"Summer Deng",
"Yuchen Hao",
"Pavan Balaji",
"Tong Geng",
"Dingwen Tao"
] |
2024-07-11T15:31:53Z
|
2024-07-05T05:55:18Z
|
2407.08597
|
Learning Program Behavioral Models from Synthesized Input-Output Pairs
|
We introduce Modelizer - a novel framework that, given a black-box program, learns a _model from its input/output behavior_ using _neural machine translation_. The resulting model _mocks_ the original program: Given an input, the model predicts the output that would have been produced by the program. However, the model is also _reversible_ - that is, the model can predict the input that would have produced a given output. Finally, the model is _differentiable_ and can be efficiently restricted to predict only a certain aspect of the program behavior. Modelizer uses _grammars_ to synthesize inputs and to parse the resulting outputs, allowing it to learn sequence-to-sequence associations between token streams. Other than input and output grammars, Modelizer only requires the ability to execute the program. The resulting models are _small_, requiring fewer than 6.3 million parameters for languages such as Markdown or HTML; and they are _accurate_, achieving up to 95.4% accuracy and a BLEU score of 0.98 with standard error 0.04 in mocking real-world applications. We foresee several _applications_ of these models, especially as the output of the program can be any aspect of program behavior. Besides mocking and predicting program behavior, the model can also synthesize inputs that are likely to produce a particular behavior, such as failures or coverage.
|
http://arxiv.org/pdf/2407.08597v1
|
[
"Tural Mammadov",
"Dietrich Klakow",
"Alexander Koller",
"Andreas Zeller"
] |
2024-07-11T15:25:02Z
|
2024-07-11T15:25:02Z
|
2407.08590
|
A Review of Nine Physics Engines for Reinforcement Learning Research
|
We present a review of popular simulation engines and frameworks used in reinforcement learning (RL) research, aiming to guide researchers in selecting tools for creating simulated physical environments for RL and training setups. It evaluates nine frameworks (Brax, Chrono, Gazebo, MuJoCo, ODE, PhysX, PyBullet, Webots, and Unity) based on their popularity, feature range, quality, usability, and RL capabilities. We highlight the challenges in selecting and utilizing physics engines for RL research, including the need for detailed comparisons and an understanding of each framework's capabilities. Key findings indicate MuJoCo as the leading framework due to its performance and flexibility, despite usability challenges. Unity is noted for its ease of use but lacks scalability and simulation fidelity. The study calls for further development to improve simulation engines' usability and performance and stresses the importance of transparency and reproducibility in RL research. This review contributes to the RL community by offering insights into the selection process for simulation engines, facilitating informed decision-making.
|
http://arxiv.org/pdf/2407.08590v1
|
[
"Michael Kaup",
"Cornelius Wolff",
"Hyerim Hwang",
"Julius Mayer",
"Elia Bruni"
] |
2024-07-11T15:13:28Z
|
2024-07-11T15:13:28Z
|
2407.08585
|
HACMan++: Spatially-Grounded Motion Primitives for Manipulation
|
Although end-to-end robot learning has shown some success for robot manipulation, the learned policies are often not sufficiently robust to variations in object pose or geometry. To improve the policy generalization, we introduce spatially-grounded parameterized motion primitives in our method HACMan++. Specifically, we propose an action representation consisting of three components: what primitive type (such as grasp or push) to execute, where the primitive will be grounded (e.g. where the gripper will make contact with the world), and how the primitive motion is executed, such as parameters specifying the push direction or grasp orientation. These three components define a novel discrete-continuous action space for reinforcement learning. Our framework enables robot agents to learn to chain diverse motion primitives together and select appropriate primitive parameters to complete long-horizon manipulation tasks. By grounding the primitives on a spatial location in the environment, our method is able to effectively generalize across object shape and pose variations. Our approach significantly outperforms existing methods, particularly in complex scenarios demanding both high-level sequential reasoning and object generalization. With zero-shot sim-to-real transfer, our policy succeeds in challenging real-world manipulation tasks, with generalization to unseen objects. Videos can be found on the project website: https://sgmp-rss2024.github.io.
|
http://arxiv.org/pdf/2407.08585v1
|
[
"Bowen Jiang",
"Yilin Wu",
"Wenxuan Zhou",
"Chris Paxton",
"David Held"
] |
2024-07-11T15:10:14Z
|
2024-07-11T15:10:14Z
|
2407.08583
|
The Synergy between Data and Multi-Modal Large Language Models: A Survey
from Co-Development Perspective
|
The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a broader spectrum of domains, attracting widespread attention due to the broader range of application scenarios. As LLMs and MLLMs rely on vast amounts of model parameters and data to achieve emergent capabilities, the importance of data is receiving increasingly widespread attention and recognition. Tracing and analyzing recent data-oriented works for MLLMs, we find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs, on the other hand, MLLMs can facilitate the development of data. The co-development of multi-modal data and MLLMs requires a clear view of 1) at which development stage of MLLMs can specific data-centric approaches be employed to enhance which capabilities, and 2) by utilizing which capabilities and acting as which roles can models contribute to multi-modal data. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective. A regularly maintained project associated with this survey is accessible at https://github.com/modelscope/data-juicer/blob/main/docs/awesome_llm_data.md.
|
http://arxiv.org/pdf/2407.08583v1
|
[
"Zhen Qin",
"Daoyuan Chen",
"Wenhao Zhang",
"Liuyi Yao",
"Yilun Huang",
"Bolin Ding",
"Yaliang Li",
"Shuiguang Deng"
] |
2024-07-11T15:08:11Z
|
2024-07-11T15:08:11Z
|
2005.11304
|
Neural Bipartite Matching
|
Graph neural networks (GNNs) have found application for learning in the space of algorithms. However, the algorithms chosen by existing research (sorting, Breadth-First search, shortest path finding, etc.) usually align perfectly with a standard GNN architecture. This report describes how neural execution is applied to a complex algorithm, such as finding maximum bipartite matching by reducing it to a flow problem and using Ford-Fulkerson to find the maximum flow. This is achieved via neural execution based only on features generated from a single GNN. The evaluation shows strongly generalising results with the network achieving optimal matching almost 100% of the time.
|
http://arxiv.org/pdf/2005.11304v4
|
[
"Dobrik Georgiev",
"Pietro Liò"
] |
2024-07-11T15:06:35Z
|
2020-05-22T17:50:38Z
|
2405.00311
|
Three-layer deep learning network random trees for fault detection in
chemical production process
|
With the development of technology, the chemical production process is becoming increasingly complex and large-scale, making fault detection particularly important. However, current detective methods struggle to address the complexities of large-scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long and short-term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three-layer deep learning network random trees (TDLN-trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher-level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method.
|
http://arxiv.org/pdf/2405.00311v2
|
[
"Ming Lu",
"Zhen Gao",
"Ying Zou",
"Zuguo Chen",
"Pei Li"
] |
2024-07-11T15:03:49Z
|
2024-05-01T04:28:44Z
|
2407.08571
|
Multi-Group Proportional Representation
|
Image search and retrieval tasks can perpetuate harmful stereotypes, erase cultural identities, and amplify social disparities. Current approaches to mitigate these representational harms balance the number of retrieved items across population groups defined by a small number of (often binary) attributes. However, most existing methods overlook intersectional groups determined by combinations of group attributes, such as gender, race, and ethnicity. We introduce Multi-Group Proportional Representation (MPR), a novel metric that measures representation across intersectional groups. We develop practical methods for estimating MPR, provide theoretical guarantees, and propose optimization algorithms to ensure MPR in retrieval. We demonstrate that existing methods optimizing for equal and proportional representation metrics may fail to promote MPR. Crucially, our work shows that optimizing MPR yields more proportional representation across multiple intersectional groups specified by a rich function class, often with minimal compromise in retrieval accuracy.
|
http://arxiv.org/pdf/2407.08571v1
|
[
"Alex Oesterling",
"Claudio Mayrink Verdun",
"Carol Xuan Long",
"Alex Glynn",
"Lucas Monteiro Paes",
"Sajani Vithana",
"Martina Cardone",
"Flavio P. Calmon"
] |
2024-07-11T14:59:17Z
|
2024-07-11T14:59:17Z
|
2407.08567
|
Adaptive Parametric Activation
|
The activation function plays a crucial role in model optimisation, yet the optimal choice remains unclear. For example, the Sigmoid activation is the de-facto activation in balanced classification tasks, however, in imbalanced classification, it proves inappropriate due to bias towards frequent classes. In this work, we delve deeper in this phenomenon by performing a comprehensive statistical analysis in the classification and intermediate layers of both balanced and imbalanced networks and we empirically show that aligning the activation function with the data distribution, enhances the performance in both balanced and imbalanced tasks. To this end, we propose the Adaptive Parametric Activation (APA) function, a novel and versatile activation function that unifies most common activation functions under a single formula. APA can be applied in both intermediate layers and attention layers, significantly outperforming the state-of-the-art on several imbalanced benchmarks such as ImageNet-LT, iNaturalist2018, Places-LT, CIFAR100-LT and LVIS and balanced benchmarks such as ImageNet1K, COCO and V3DET. The code is available at https://github.com/kostas1515/AGLU.
|
http://arxiv.org/pdf/2407.08567v1
|
[
"Konstantinos Panagiotis Alexandridis",
"Jiankang Deng",
"Anh Nguyen",
"Shan Luo"
] |
2024-07-11T14:57:27Z
|
2024-07-11T14:57:27Z
|
2407.08560
|
Causal inference through multi-stage learning and doubly robust deep
neural networks
|
Deep neural networks (DNNs) have demonstrated remarkable empirical performance in large-scale supervised learning problems, particularly in scenarios where both the sample size $n$ and the dimension of covariates $p$ are large. This study delves into the application of DNNs across a wide spectrum of intricate causal inference tasks, where direct estimation falls short and necessitates multi-stage learning. Examples include estimating the conditional average treatment effect and dynamic treatment effect. In this framework, DNNs are constructed sequentially, with subsequent stages building upon preceding ones. To mitigate the impact of estimation errors from early stages on subsequent ones, we integrate DNNs in a doubly robust manner. In contrast to previous research, our study offers theoretical assurances regarding the effectiveness of DNNs in settings where the dimensionality $p$ expands with the sample size. These findings are significant independently and extend to degenerate single-stage learning problems.
|
http://arxiv.org/pdf/2407.08560v1
|
[
"Yuqian Zhang",
"Jelena Bradic"
] |
2024-07-11T14:47:44Z
|
2024-07-11T14:47:44Z
|
2312.06837
|
Spectral State Space Models
|
This paper studies sequence modeling for prediction tasks with long range dependencies. We propose a new formulation for state space models (SSMs) based on learning linear dynamical systems with the spectral filtering algorithm (Hazan et al. (2017)). This gives rise to a novel sequence prediction architecture we call a spectral state space model. Spectral state space models have two primary advantages. First, they have provable robustness properties as their performance depends on neither the spectrum of the underlying dynamics nor the dimensionality of the problem. Second, these models are constructed with fixed convolutional filters that do not require learning while still outperforming SSMs in both theory and practice. The resulting models are evaluated on synthetic dynamical systems and long-range prediction tasks of various modalities. These evaluations support the theoretical benefits of spectral filtering for tasks requiring very long range memory.
|
http://arxiv.org/pdf/2312.06837v4
|
[
"Naman Agarwal",
"Daniel Suo",
"Xinyi Chen",
"Elad Hazan"
] |
2024-07-11T14:35:23Z
|
2023-12-11T21:00:28Z
|
2406.08229
|
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
|
In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional models, typically reliant on a static training-test approach, struggle to keep pace with these dynamic demands. Streaming recommendation, particularly through continual graph learning, has emerged as a novel solution. However, existing methods in this area either rely on historical data replay, which is increasingly impractical due to stringent data privacy regulations; or are inability to effectively address the over-stability issue; or depend on model-isolation and expansion strategies. To tackle these difficulties, we present GPT4Rec, a Graph Prompt Tuning method for streaming Recommendation. Given the evolving user-item interaction graph, GPT4Rec first disentangles the graph patterns into multiple views. After isolating specific interaction patterns and relationships in different views, GPT4Rec utilizes lightweight graph prompts to efficiently guide the model across varying interaction patterns within the user-item graph. Firstly, node-level prompts are employed to instruct the model to adapt to changes in the attributes or properties of individual nodes within the graph. Secondly, structure-level prompts guide the model in adapting to broader patterns of connectivity and relationships within the graph. Finally, view-level prompts are innovatively designed to facilitate the aggregation of information from multiple disentangled views. These prompt designs allow GPT4Rec to synthesize a comprehensive understanding of the graph, ensuring that all vital aspects of the user-item interactions are considered and effectively integrated. Experiments on four diverse real-world datasets demonstrate the effectiveness and efficiency of our proposal.
|
http://arxiv.org/abs/2406.08229v2
|
[
"Peiyan Zhang",
"Yuchen Yan",
"Xi Zhang",
"Liying Kang",
"Chaozhuo Li",
"Feiran Huang",
"Senzhang Wang",
"Sunghun Kim"
] |
2024-07-11T14:33:23Z
|
2024-06-12T13:59:31Z
|
2407.08546
|
Quantitative Evaluation of the Saliency Map for Alzheimer's Disease
Classifier with Anatomical Segmentation
|
Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has multiple subtypes, the pathological mechanism of AD remains not fully understood and may vary from patient to patient. Due to the lack of such understanding, it is difficult to comprehensively and effectively assess the saliency map of AD classifier. In this paper, we utilize the anatomical segmentation to allocate saliency values into different brain regions. By plotting the distributions of saliency maps corresponding to AD and NC (Normal Control), we can gain a comprehensive view of the model's decisions process. In order to leverage the fact that the brain volume shrinkage happens in AD patients during disease progression, we define a new evaluation metric, brain volume change score (VCS), by computing the average Pearson correlation of the brain volume changes and the saliency values of a model in different brain regions for each patient. Thus, the VCS metric can help us gain some knowledge of how saliency maps resulting from different models relate to the changes of the volumes across different regions in the whole brain. We trained candidate models on the ADNI dataset and tested on three different datasets. Our results indicate: (i) models with higher VCSs tend to demonstrate saliency maps with more details relevant to the AD pathology, (ii) using gradient-based adversarial training strategies such as FGSM and stochastic masking can improve the VCSs of the models.
|
http://arxiv.org/pdf/2407.08546v1
|
[
"Yihan Zhang",
"Xuanshuo Zhang",
"Wei Wu",
"Haohan Wang"
] |
2024-07-11T14:30:49Z
|
2024-07-11T14:30:49Z
|
2407.03961
|
Leveraging Latent Diffusion Models for Training-Free In-Distribution
Data Augmentation for Surface Defect Detection
|
Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. State-of-the-art data augmentation procedures add synthetic defect data by superimposing artifacts to normal samples to mitigate problems related to unbalanced training data. These techniques often produce out-of-distribution images, resulting in systems that learn what is not a normal sample but cannot accurately identify what a defect looks like. In this work, we introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model through text descriptions and region localization of the possible anomalies. This strategic shift enhances the interpretability of results and fosters a more robust human feedback loop, facilitating iterative improvements of the generated outputs. Remarkably, our approach operates in a zero-shot manner, avoiding time-consuming fine-tuning procedures while achieving superior performance. We demonstrate the efficacy and versatility of DIAG with respect to state-of-the-art data augmentation approaches on the challenging KSDD2 dataset, with an improvement in AP of approximately 18% when positive samples are available and 28% when they are missing. The source code is available at https://github.com/intelligolabs/DIAG.
|
http://arxiv.org/pdf/2407.03961v2
|
[
"Federico Girella",
"Ziyue Liu",
"Franco Fummi",
"Francesco Setti",
"Marco Cristani",
"Luigi Capogrosso"
] |
2024-07-11T14:14:22Z
|
2024-07-04T14:28:52Z
|
2212.02075
|
Differentiated Federated Reinforcement Learning Based Traffic Offloading
on Space-Air-Ground Integrated Networks
|
The Space-Air-Ground Integrated Network (SAGIN) plays a pivotal role as a comprehensive foundational network communication infrastructure, presenting opportunities for highly efficient global data transmission. Nonetheless, given SAGIN's unique characteristics as a dynamically heterogeneous network, conventional network optimization methodologies encounter challenges in satisfying the stringent requirements for network latency and stability inherent to data transmission within this network environment. Therefore, this paper proposes the use of differentiated federated reinforcement learning (DFRL) to solve the traffic offloading problem in SAGIN, i.e., using multiple agents to generate differentiated traffic offloading policies. Considering the differentiated characteristics of each region of SAGIN, DFRL models the traffic offloading policy optimization process as the process of solving the Decentralized Partially Observable Markov Decision Process (DEC-POMDP) problem. The paper proposes a novel Differentiated Federated Soft Actor-Critic (DFSAC) algorithm to solve the problem. The DFSAC algorithm takes the network packet delay as the joint reward value and introduces the global trend model as the joint target action-value function of each agent to guide the update of each agent's policy. The simulation results demonstrate that the traffic offloading policy based on the DFSAC algorithm achieves better performance in terms of network throughput, packet loss rate, and packet delay compared to the traditional federated reinforcement learning approach and other baseline approaches.
|
http://arxiv.org/abs/2212.02075v3
|
[
"Yeguang Qin",
"Yilin Yang",
"Fengxiao Tang",
"Xin Yao",
"Ming Zhao",
"Nei Kato"
] |
2024-07-11T14:11:23Z
|
2022-12-05T07:40:29Z
|
2402.06590
|
Predictive representations: building blocks of intelligence
|
Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This paper integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation (SR) and its generalizations, which have been widely applied both as engineering tools and models of brain function. This convergence suggests that particular kinds of predictive representations may function as versatile building blocks of intelligence.
|
http://arxiv.org/pdf/2402.06590v3
|
[
"Wilka Carvalho",
"Momchil S. Tomov",
"William de Cothi",
"Caswell Barry",
"Samuel J. Gershman"
] |
2024-07-11T14:02:37Z
|
2024-02-09T18:10:38Z
|
2403.00942
|
Resilience of Entropy Model in Distributed Neural Networks
|
Distributed deep neural networks (DNNs) have emerged as a key technique to reduce communication overhead without sacrificing performance in edge computing systems. Recently, entropy coding has been introduced to further reduce the communication overhead. The key idea is to train the distributed DNN jointly with an entropy model, which is used as side information during inference time to adaptively encode latent representations into bit streams with variable length. To the best of our knowledge, the resilience of entropy models is yet to be investigated. As such, in this paper we formulate and investigate the resilience of entropy models to intentional interference (e.g., adversarial attacks) and unintentional interference (e.g., weather changes and motion blur). Through an extensive experimental campaign with 3 different DNN architectures, 2 entropy models and 4 rate-distortion trade-off factors, we demonstrate that the entropy attacks can increase the communication overhead by up to 95%. By separating compression features in frequency and spatial domain, we propose a new defense mechanism that can reduce the transmission overhead of the attacked input by about 9% compared to unperturbed data, with only about 2% accuracy loss. Importantly, the proposed defense mechanism is a standalone approach which can be applied in conjunction with approaches such as adversarial training to further improve robustness. Code will be shared for reproducibility.
|
http://arxiv.org/pdf/2403.00942v2
|
[
"Milin Zhang",
"Mohammad Abdi",
"Shahriar Rifat",
"Francesco Restuccia"
] |
2024-07-11T13:51:56Z
|
2024-03-01T19:39:19Z
|
2402.08616
|
Adjustment Identification Distance: A gadjid for Causal Structure
Learning
|
Evaluating graphs learned by causal discovery algorithms is difficult: The number of edges that differ between two graphs does not reflect how the graphs differ with respect to the identifying formulas they suggest for causal effects. We introduce a framework for developing causal distances between graphs which includes the structural intervention distance for directed acyclic graphs as a special case. We use this framework to develop improved adjustment-based distances as well as extensions to completed partially directed acyclic graphs and causal orders. We develop new reachability algorithms to compute the distances efficiently and to prove their low polynomial time complexity. In our package gadjid (open source at https://github.com/CausalDisco/gadjid), we provide implementations of our distances; they are orders of magnitude faster with proven lower time complexity than the structural intervention distance and thereby provide a success metric for causal discovery that scales to graph sizes that were previously prohibitive.
|
http://arxiv.org/pdf/2402.08616v2
|
[
"Leonard Henckel",
"Theo Würtzen",
"Sebastian Weichwald"
] |
2024-07-11T13:45:33Z
|
2024-02-13T17:32:59Z
|
2407.08500
|
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time
Dynamic Graph Mode
|
Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challenges stemming from noise and limited historical data. Graph Data Augmentation (GDA) emerges as a critical solution, yet current approaches primarily focus on static graphs and struggle to effectively address the dynamics inherent in CTDGs. Moreover, these methods often demand substantial domain expertise for parameter tuning and lack theoretical guarantees for augmentation efficacy. To address these issues, we propose Conda, a novel latent diffusion-based GDA method tailored for CTDGs. Conda features a sandwich-like architecture, incorporating a Variational Auto-Encoder (VAE) and a conditional diffusion model, aimed at generating enhanced historical neighbor embeddings for target nodes. Unlike conventional diffusion models trained on entire graphs via pre-training, Conda requires historical neighbor sequence embeddings of target nodes for training, thus facilitating more targeted augmentation. We integrate Conda into the CTDG model and adopt an alternating training strategy to optimize performance. Extensive experimentation across six widely used real-world datasets showcases the consistent performance improvement of our approach, particularly in scenarios with limited historical data.
|
http://arxiv.org/pdf/2407.08500v1
|
[
"Yuxing Tian",
"Yiyan Qi",
"Aiwen Jiang",
"Qi Huang",
"Jian Guo"
] |
2024-07-11T13:35:22Z
|
2024-07-11T13:35:22Z
|
2406.12008
|
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining
of Random Forest
|
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data to maintain model accuracy. This is particularly important in applications where data is periodically and sequentially generated over time in data streams, such as auto-driving systems, and credit card payments. In this setting, performing periodic model retraining with the old and new data accumulated is beneficial as it fully captures possible drifts in the data distribution over time. However, this is unpractical with state-of-the-art classical algorithms for RF as they scale linearly with the accumulated number of samples. We propose QC-Forest, a classical-quantum algorithm designed to time-efficiently retrain RF models in the streaming setting for multi-class classification and regression, achieving a runtime poly-logarithmic in the total number of accumulated samples. QC-Forest leverages Des-q, a quantum algorithm for single tree construction and retraining proposed by Kumar et al. by expanding to multi-class classification, as the original proposal was limited to binary classes, and introducing an exact classical method to replace an underlying quantum subroutine incurring a finite error, while maintaining the same poly-logarithmic dependence. Finally, we showcase that QC-Forest achieves competitive accuracy in comparison to state-of-the-art RF methods on widely used benchmark datasets with up to 80,000 samples, while significantly speeding up the model retrain.
|
http://arxiv.org/pdf/2406.12008v3
|
[
"Romina Yalovetzky",
"Niraj Kumar",
"Changhao Li",
"Marco Pistoia"
] |
2024-07-11T13:32:59Z
|
2024-06-17T18:21:03Z
|
2305.08752
|
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall
Activity Annotations from Wearable Sensors
|
Research into the detection of human activities from wearable sensors is a highly active field, benefiting numerous applications, from ambulatory monitoring of healthcare patients via fitness coaching to streamlining manual work processes. We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-the-wild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels. Our study illustrates that different labeling methodologies directly impact the annotations' quality, as well as the capabilities of a deep learning classifier trained with the data. We noticed that in situ methods produce less but more precise labels than recall methods. Furthermore, we combined an activity diary with a visualization tool that enables the participant to inspect and label their activity data. Due to the introduction of such a tool were able to decrease missing annotations and increase the annotation consistency, and therefore the F1-Score of the deep learning model by up to 8% (ranging between 82.1 and 90.4% F1-Score). Furthermore, we discuss the advantages and disadvantages of the methods compared in our study, the biases they could introduce, and the consequences of their usage on human activity recognition studies as well as possible solutions.
|
http://arxiv.org/abs/2305.08752v3
|
[
"Alexander Hoelzemann",
"Kristof Van Laerhoven"
] |
2024-07-11T13:23:32Z
|
2023-05-15T16:02:56Z
|
2407.08479
|
Robust Generalization of Graph Neural Networks for Carrier Scheduling
|
Battery-free sensor tags are devices that leverage backscatter techniques to communicate with standard IoT devices, thereby augmenting a network's sensing capabilities in a scalable way. For communicating, a sensor tag relies on an unmodulated carrier provided by a neighboring IoT device, with a schedule coordinating this provisioning across the network. Carrier scheduling--computing schedules to interrogate all sensor tags while minimizing energy, spectrum utilization, and latency--is an NP-Hard optimization problem. Recent work introduces learning-based schedulers that achieve resource savings over a carefully-crafted heuristic, generalizing to networks of up to 60 nodes. However, we find that their advantage diminishes in networks with hundreds of nodes, and degrades further in larger setups. This paper introduces RobustGANTT, a GNN-based scheduler that improves generalization (without re-training) to networks up to 1000 nodes (100x training topology sizes). RobustGANTT not only achieves better and more consistent generalization, but also computes schedules requiring up to 2x less resources than existing systems. Our scheduler exhibits average runtimes of hundreds of milliseconds, allowing it to react fast to changing network conditions. Our work not only improves resource utilization in large-scale backscatter networks, but also offers valuable insights in learning-based scheduling.
|
http://arxiv.org/pdf/2407.08479v1
|
[
"Daniel F. Perez-Ramirez",
"Carlos Pérez-Penichet",
"Nicolas Tsiftes",
"Dejan Kostic",
"Magnus Boman",
"Thiemo Voigt"
] |
2024-07-11T13:13:24Z
|
2024-07-11T13:13:24Z
|
2406.06566
|
Natural Language Interaction with a Household Electricity
Knowledge-based Digital Twin
|
Domain specific digital twins, representing a digital replica of various segments of the smart grid, are foreseen as able to model, simulate, and control the respective segments. At the same time, knowledge-based digital twins, coupled with AI, may also empower humans to understand aspects of the system through natural language interaction in view of planning and policy making. This paper is the first to assess and report on the potential of Retrieval Augmented Generation (RAG) question answers related to household electrical energy measurement aspects leveraging a knowledge-based energy digital twin. Relying on the recently published electricity consumption knowledge graph that actually represents a knowledge-based digital twin, we study the capabilities of ChatGPT, Gemini and Llama in answering electricity related questions. Furthermore, we compare the answers with the ones generated through a RAG techniques that leverages an existing electricity knowledge-based digital twin. Our findings illustrate that the RAG approach not only reduces the incidence of incorrect information typically generated by LLMs but also significantly improves the quality of the output by grounding responses in verifiable data. This paper details our methodology, presents a comparative analysis of responses with and without RAG, and discusses the implications of our findings for future applications of AI in specialized sectors like energy data analysis.
|
http://arxiv.org/pdf/2406.06566v2
|
[
"Carolina Fortuna",
"Vid Hanžel",
"Blaž Bertalanič"
] |
2024-07-11T13:05:37Z
|
2024-06-03T07:44:32Z
|
2407.08464
|
TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware
Representations
|
Unsupervised goal-conditioned reinforcement learning (GCRL) is a promising paradigm for developing diverse robotic skills without external supervision. However, existing unsupervised GCRL methods often struggle to cover a wide range of states in complex environments due to their limited exploration and sparse or noisy rewards for GCRL. To overcome these challenges, we propose a novel unsupervised GCRL method that leverages TemporaL Distance-aware Representations (TLDR). TLDR selects faraway goals to initiate exploration and computes intrinsic exploration rewards and goal-reaching rewards, based on temporal distance. Specifically, our exploration policy seeks states with large temporal distances (i.e. covering a large state space), while the goal-conditioned policy learns to minimize the temporal distance to the goal (i.e. reaching the goal). Our experimental results in six simulated robotic locomotion environments demonstrate that our method significantly outperforms previous unsupervised GCRL methods in achieving a wide variety of states.
|
http://arxiv.org/pdf/2407.08464v1
|
[
"Junik Bae",
"Kwanyoung Park",
"Youngwoon Lee"
] |
2024-07-11T13:01:18Z
|
2024-07-11T13:01:18Z
|
2407.08462
|
Distributed Deep Reinforcement Learning Based Gradient Quantization for
Federated Learning Enabled Vehicle Edge Computing
|
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.
|
http://arxiv.org/pdf/2407.08462v1
|
[
"Cui Zhang",
"Wenjun Zhang",
"Qiong Wu",
"Pingyi Fan",
"Qiang Fan",
"Jiangzhou Wang",
"Khaled B. Letaief"
] |
2024-07-11T12:58:47Z
|
2024-07-11T12:58:47Z
|
2407.08459
|
Graph Expansions of Deep Neural Networks and their Universal Scaling
Limits
|
We present a unified approach to obtain scaling limits of neural networks using the genus expansion technique from random matrix theory. This approach begins with a novel expansion of neural networks which is reminiscent of Butcher series for ODEs, and is obtained through a generalisation of Fa`a di Bruno's formula to an arbitrary number of compositions. In this expansion, the role of monomials is played by random multilinear maps indexed by directed graphs whose edges correspond to random matrices, which we call operator graphs. This expansion linearises the effect of the activation functions, allowing for the direct application of Wick's principle to compute the expectation of each of its terms. We then determine the leading contribution to each term by embedding the corresponding graphs onto surfaces, and computing their Euler characteristic. Furthermore, by developing a correspondence between analytic and graphical operations, we obtain similar graph expansions for the neural tangent kernel as well as the input-output Jacobian of the original neural network, and derive their infinite-width limits with relative ease. Notably, we find explicit formulae for the moments of the limiting singular value distribution of the Jacobian. We then show that all of these results hold for networks with more general weights, such as general matrices with i.i.d. entries satisfying moment assumptions, complex matrices and sparse matrices.
|
http://arxiv.org/pdf/2407.08459v1
|
[
"Nicola Muca Cirone",
"Jad Hamdan",
"Cristopher Salvi"
] |
2024-07-11T12:58:07Z
|
2024-07-11T12:58:07Z
|
2407.08458
|
Joint Optimization of Age of Information and Energy Consumption in
NR-V2X System based on Deep Reinforcement Learning
|
Autonomous driving may be the most important application scenario of next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancement in cellular V2X (C-V2X) with improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur, and thus degrade the age of information (AOI). Therefore, a interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation interval (RRI), higher-frequency transmissions take ore energy to reduce AoI. Hence, it is important to jointly consider AoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations have demonstrated the performance of our proposed algorithm.
|
http://arxiv.org/pdf/2407.08458v1
|
[
"Shulin Song",
"Zheng Zhang",
"Qiong Wu",
"Qiang Fan",
"Pingyi Fan"
] |
2024-07-11T12:54:38Z
|
2024-07-11T12:54:38Z
|
2404.08108
|
Protein intrinsic disorder prediction using Attention U-Net and
ProtTrans protein language model
|
The prediction of intrinsic disorder regions has significant implications for understanding protein function, structure, and dynamics. It can help to discover novel functions or protein-protein interactions essential to designing new drugs, therapies, or enzymes. Recently, a new generation of predictors based on protein language models is emerging. These algorithms reach state-of-the-art accuracy without calculating time-consuming multiple sequence alignments (MSAs). The article pre-sents a new protein intrinsic disorder predictor DisorderUnetLM based on the Attention U-Net convolutional neural network using features from the protein language model ProtTrans. DisorderUnetLM shows top results in the direct comparison with flDPnn and IDP-CRF predictors using MSAs and with the SETH predictor using features from the same ProtTrans model. Moreover, among 41 predictors from the latest Critical Assessment of Protein Intrinsic Disorder Prediction (CAID-2) benchmark, it ranks 9th for the Disorder-PDB subset (with ROC-AUC of 0.924) and 1st for the Disorder-NOX subset (with ROC-AUC of 0.844) which confirms its potential to perform well in the upcoming CAID-3 challenge for which Disor-derUnetLM was submitted.
|
http://arxiv.org/pdf/2404.08108v2
|
[
"Krzysztof Kotowski",
"Irena Roterman",
"Katarzyna Stapor"
] |
2024-07-11T12:41:51Z
|
2024-04-11T20:14:14Z
|
2407.05206
|
Helios: An extremely low power event-based gesture recognition for
always-on smart eyewear
|
This paper introduces Helios, the first extremely low-power, real-time, event-based hand gesture recognition system designed for all-day on smart eyewear. As augmented reality (AR) evolves, current smart glasses like the Meta Ray-Bans prioritize visual and wearable comfort at the expense of functionality. Existing human-machine interfaces (HMIs) in these devices, such as capacitive touch and voice controls, present limitations in ergonomics, privacy and power consumption. Helios addresses these challenges by leveraging natural hand interactions for a more intuitive and comfortable user experience. Our system utilizes a extremely low-power and compact 3mmx4mm/20mW event camera to perform natural hand-based gesture recognition for always-on smart eyewear. The camera's output is processed by a convolutional neural network (CNN) running on a NXP Nano UltraLite compute platform, consuming less than 350mW. Helios can recognize seven classes of gestures, including subtle microgestures like swipes and pinches, with 91% accuracy. We also demonstrate real-time performance across 20 users at a remarkably low latency of 60ms. Our user testing results align with the positive feedback we received during our recent successful demo at AWE-USA-2024.
|
http://arxiv.org/pdf/2407.05206v2
|
[
"Prarthana Bhattacharyya",
"Joshua Mitton",
"Ryan Page",
"Owen Morgan",
"Ben Menzies",
"Gabriel Homewood",
"Kemi Jacobs",
"Paolo Baesso",
"Dave Trickett",
"Chris Mair",
"Taru Muhonen",
"Rory Clark",
"Louis Berridge",
"Richard Vigars",
"Iain Wallace"
] |
2024-07-11T12:33:53Z
|
2024-07-06T23:16:41Z
|
2407.08442
|
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical
Time-Series Imputation
|
We introduce a novel classification framework for time-series imputation using deep learning, with a particular focus on clinical data. By identifying conceptual gaps in the literature and existing reviews, we devise a taxonomy grounded on the inductive bias of neural imputation frameworks, resulting in a classification of existing deep imputation strategies based on their suitability for specific imputation scenarios and data-specific properties. Our review further examines the existing methodologies employed to benchmark deep imputation models, evaluating their effectiveness in capturing the missingness scenarios found in clinical data and emphasising the importance of reconciling mathematical abstraction with clinical insights. Our classification aims to serve as a guide for researchers to facilitate the selection of appropriate deep learning imputation techniques tailored to their specific clinical data. Our novel perspective also highlights the significance of bridging the gap between computational methodologies and medical insights to achieve clinically sound imputation models.
|
http://arxiv.org/pdf/2407.08442v1
|
[
"Linglong Qian",
"Tao Wang",
"Jun Wang",
"Hugh Logan Ellis",
"Robin Mitra",
"Richard Dobson",
"Zina Ibrahim"
] |
2024-07-11T12:33:28Z
|
2024-07-11T12:33:28Z
|
2407.08434
|
Improve Load Forecasting in Energy Communities through Transfer Learning
using Open-Access Synthetic Profiles
|
According to a conservative estimate, a 1% reduction in forecast error for a 10 GW energy utility can save up to $ 1.6 million annually. In our context, achieving precise forecasts of future power consumption is crucial for operating flexible energy assets using model predictive control approaches. Specifically, this work focuses on the load profile forecast of a first-year energy community with the common practical challenge of limited historical data availability. We propose to pre-train the load prediction models with open-access synthetic load profiles using transfer learning techniques to tackle this challenge. Results show that this approach improves both, the training stability and prediction error. In a test case with 74 households, the prediction mean squared error (MSE) decreased from 0.34 to 0.13, showing transfer learning based on synthetic load profiles to be a viable approach to compensate for a lack of historic data.
|
http://arxiv.org/pdf/2407.08434v1
|
[
"Lukas Moosbrugger",
"Valentin Seiler",
"Gerhard Huber",
"Peter Kepplinger"
] |
2024-07-11T12:17:31Z
|
2024-07-11T12:17:31Z
|
2310.02206
|
Chunking: Continual Learning is not just about Distribution Shift
|
Work on continual learning (CL) has thus far largely focused on the problems arising from shifts in the data distribution. However, CL can be decomposed into two sub-problems: (a) shifts in the data distribution, and (b) dealing with the fact that the data is split into chunks and so only a part of the data is available to be trained on at any point in time. In this work, we look at the latter sub-problem, the chunking of data. We show that chunking is an important part of CL, accounting for around half of the performance drop from offline learning in our experiments. Furthermore, our results reveal that current CL algorithms do not address the chunking sub-problem, only performing as well as plain SGD training when there is no shift in the data distribution. Therefore, we show that chunking is both an important and currently unaddressed sub-problem and until it is addressed CL methods will be capped in performance. Additionally, we analyse why performance drops when learning occurs on identically distributed chunks of data, and find that forgetting, which is often seen to be a problem due to distribution shift, still arises and is a significant problem. We also show that performance on the chunking sub-problem can be increased and that this performance transfers to the full CL setting, where there is distribution shift. Hence, we argue that work on chunking can help advance CL in general.
|
http://arxiv.org/pdf/2310.02206v2
|
[
"Thomas L. Lee",
"Amos Storkey"
] |
2024-07-11T12:13:38Z
|
2023-10-03T17:04:33Z
|
2302.03460
|
Mind the Gap! Bridging Explainable Artificial Intelligence and Human
Understanding with Luhmann's Functional Theory of Communication
|
Over the past decade explainable artificial intelligence has evolved from a predominantly technical discipline into a field that is deeply intertwined with social sciences. Insights such as human preference for contrastive -- more precisely, counterfactual -- explanations have played a major role in this transition, inspiring and guiding the research in computer science. Other observations, while equally important, have nevertheless received much less consideration. The desire of human explainees to communicate with artificial intelligence explainers through a dialogue-like interaction has been mostly neglected by the community. This poses many challenges for the effectiveness and widespread adoption of such technologies as delivering a single explanation optimised according to some predefined objectives may fail to engender understanding in its recipients and satisfy their unique needs given the diversity of human knowledge and intention. Using insights elaborated by Niklas Luhmann and, more recently, Elena Esposito we apply social systems theory to highlight challenges in explainable artificial intelligence and offer a path forward, striving to reinvigorate the technical research in the direction of interactive and iterative explainers. Specifically, this paper demonstrates the potential of systems theoretical approaches to communication in elucidating and addressing the problems and limitations of human-centred explainable artificial intelligence.
|
http://arxiv.org/pdf/2302.03460v3
|
[
"Bernard Keenan",
"Kacper Sokol"
] |
2024-07-11T12:13:04Z
|
2023-02-07T13:31:02Z
|
2407.08432
|
Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy
|
Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during RT, allowing for inter-fraction, and perhaps even intra-fraction, adjustments of treatment plans. However, achieving this requires fast and accurate dose calculations. While Monte Carlo simulations offer accuracy, they are computationally intensive. Deep learning frameworks show promise, yet lack uncertainty quantification crucial for high-risk applications like RT. Risk-controlling prediction sets (RCPS) offer model-agnostic uncertainty quantification with mathematical guarantees. However, we show that naive application of RCPS may lead to only certain subgroups such as the image background being risk-controlled. In this work, we extend RCPS to provide prediction intervals with coverage guarantees for multiple subgroups with unknown subgroup membership at test time. We evaluate our algorithm on real clinical planing volumes from five different anatomical regions and show that our novel subgroup RCPS (SG-RCPS) algorithm leads to prediction intervals that jointly control the risk for multiple subgroups. In particular, our method controls the risk of the crucial voxels along the radiation beam significantly better than conventional RCPS.
|
http://arxiv.org/pdf/2407.08432v1
|
[
"Paul Fischer",
"Hannah Willms",
"Moritz Schneider",
"Daniela Thorwarth",
"Michael Muehlebach",
"Christian F. Baumgartner"
] |
2024-07-11T12:12:55Z
|
2024-07-11T12:12:55Z
|
2407.00107
|
WineGraph: A Graph Representation For Food-Wine Pairing
|
We present WineGraph, an extended version of FlavorGraph, a heterogeneous graph incorporating wine data into its structure. This integration enables food-wine pairing based on taste and sommelier-defined rules. Leveraging a food dataset comprising 500,000 reviews and a wine reviews dataset with over 130,000 entries, we computed taste descriptors for both food and wine. This information was then utilised to pair food items with wine and augment FlavorGraph with additional data. The results demonstrate the potential of heterogeneous graphs to acquire supplementary information, proving beneficial for wine pairing.
|
http://arxiv.org/pdf/2407.00107v2
|
[
"Zuzanna Gawrysiak",
"Agata Żywot",
"Agnieszka Ławrynowicz"
] |
2024-07-11T12:12:48Z
|
2024-06-27T11:11:19Z
|
2404.11269
|
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in
Multivariate Time Series
|
In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging labeled data from a related domain to detect anomalies in an unlabeled target domain. However, existing UDA methods assume consistent anomalous classes across domains. To address this limitation, we propose a novel Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness. Additionally, our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain, ensuring comprehensive feature representation learning and domain-invariant feature extraction. Finally, an effective Centre-based Entropy Classifier (CEC) accurately learns normal boundaries in the source domain. Extensive evaluations on multiple real-world datasets and a synthetic dataset highlight DACAD's superior performance in transferring knowledge across domains and mitigating the challenge of limited labeled data in TSAD.
|
http://arxiv.org/pdf/2404.11269v2
|
[
"Zahra Zamanzadeh Darban",
"Yiyuan Yang",
"Geoffrey I. Webb",
"Charu C. Aggarwal",
"Qingsong Wen",
"Mahsa Salehi"
] |
2024-07-11T12:04:13Z
|
2024-04-17T11:20:14Z
|
2401.13537
|
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy
Physics Foundation Models
|
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.
|
http://arxiv.org/pdf/2401.13537v3
|
[
"Tobias Golling",
"Lukas Heinrich",
"Michael Kagan",
"Samuel Klein",
"Matthew Leigh",
"Margarita Osadchy",
"John Andrew Raine"
] |
2024-07-11T11:55:25Z
|
2024-01-24T15:46:32Z
|
2301.10879
|
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for
On-Device Inference
|
Neural Architecture Search (NAS) for Federated Learning (FL) is an emerging field. It automates the design and training of Deep Neural Networks (DNNs) when data cannot be centralized due to privacy, communication costs, or regulatory restrictions. Recent federated NAS methods not only reduce manual effort but also help achieve higher accuracy than traditional FL methods like FedAvg. Despite the success, existing federated NAS methods still fall short in satisfying diverse deployment targets common in on-device inference like hardware, latency budgets, or variable battery levels. Most federated NAS methods search for only a limited range of neuro-architectural patterns, repeat them in a DNN, thereby restricting achievable performance. Moreover, these methods incur prohibitive training costs to satisfy deployment targets. They perform the training and search of DNN architectures repeatedly for each case. SuperFedNAS addresses these challenges by decoupling the training and search in federated NAS. SuperFedNAS co-trains a large number of diverse DNN architectures contained inside one supernet in the FL setting. Post-training, clients perform NAS locally to find specialized DNNs by extracting different parts of the trained supernet with no additional training. SuperFedNAS takes O(1) (instead of O(N)) cost to find specialized DNN architectures in FL for any N deployment targets. As part of SuperFedNAS, we introduce MaxNet - a novel FL training algorithm that performs multi-objective federated optimization of a large number of DNN architectures ($approx 5*10^8$) under different client data distributions. Overall, SuperFedNAS achieves upto 37.7% higher accuracy for the same MACs or upto 8.13x reduction in MACs for the same accuracy than existing federated NAS methods.
|
http://arxiv.org/pdf/2301.10879v3
|
[
"Alind Khare",
"Animesh Agrawal",
"Aditya Annavajjala",
"Payman Behnam",
"Myungjin Lee",
"Hugo Latapie",
"Alexey Tumanov"
] |
2024-07-11T11:53:21Z
|
2023-01-26T00:17:10Z
|
2407.08417
|
Unveiling the Potential of BERTopic for Multilingual Fake News Analysis
-- Use Case: Covid-19
|
Topic modeling is frequently being used for analysing large text corpora such as news articles or social media data. BERTopic, consisting of sentence embedding, dimension reduction, clustering, and topic extraction, is the newest and currently the SOTA topic modeling method. However, current topic modeling methods have room for improvement because, as unsupervised methods, they require careful tuning and selection of hyperparameters, e.g., for dimension reduction and clustering. This paper aims to analyse the technical application of BERTopic in practice. For this purpose, it compares and selects different methods and hyperparameters for each stage of BERTopic through density based clustering validation and six different topic coherence measures. Moreover, it also aims to analyse the results of topic modeling on real world data as a use case. For this purpose, the German fake news dataset (GermanFakeNCovid) on Covid-19 was created by us and in order to experiment with topic modeling in a multilingual (English and German) setting combined with the FakeCovid dataset. With the final results, we were able to determine thematic similarities between the United States and Germany. Whereas, distinguishing the topics of fake news from India proved to be more challenging.
|
http://arxiv.org/pdf/2407.08417v1
|
[
"Karla Schäfer",
"Jeong-Eun Choi",
"Inna Vogel",
"Martin Steinebach"
] |
2024-07-11T11:47:43Z
|
2024-07-11T11:47:43Z
|
2407.08415
|
Parallelizing Autoregressive Generation with Variational State Space
Models
|
Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to their autoregressiveness. We propose the variational SSM (VSSM), a variational autoencoder (VAE) where both the encoder and decoder are SSMs. Since sampling the latent variables and decoding them with the SSM can be parallelized, both training and generation can be conducted in parallel. Moreover, the decoder recurrence allows generation to be resumed without reprocessing the whole sequence. Finally, we propose the autoregressive VSSM that can be conditioned on a partial realization of the sequence, as is common in language generation tasks. Interestingly, the autoregressive VSSM still enables parallel generation. We highlight on toy problems (MNIST, CIFAR) the empirical gains in speed-up and show that it competes with traditional models in terms of generation quality (Transformer, Mamba SSM).
|
http://arxiv.org/pdf/2407.08415v1
|
[
"Gaspard Lambrechts",
"Yann Claes",
"Pierre Geurts",
"Damien Ernst"
] |
2024-07-11T11:41:29Z
|
2024-07-11T11:41:29Z
|
2407.06315
|
Shedding More Light on Robust Classifiers under the lens of Energy-based
Models
|
By reinterpreting a robust discriminative classifier as Energy-based Model (EBM), we offer a new take on the dynamics of adversarial training (AT). Our analysis of the energy landscape during AT reveals that untargeted attacks generate adversarial images much more in-distribution (lower energy) than the original data from the point of view of the model. Conversely, we observe the opposite for targeted attacks. On the ground of our thorough analysis, we present new theoretical and practical results that show how interpreting AT energy dynamics unlocks a better understanding: (1) AT dynamic is governed by three phases and robust overfitting occurs in the third phase with a drastic divergence between natural and adversarial energies (2) by rewriting the loss of TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization (TRADES) in terms of energies, we show that TRADES implicitly alleviates overfitting by means of aligning the natural energy with the adversarial one (3) we empirically show that all recent state-of-the-art robust classifiers are smoothing the energy landscape and we reconcile a variety of studies about understanding AT and weighting the loss function under the umbrella of EBMs. Motivated by rigorous evidence, we propose Weighted Energy Adversarial Training (WEAT), a novel sample weighting scheme that yields robust accuracy matching the state-of-the-art on multiple benchmarks such as CIFAR-10 and SVHN and going beyond in CIFAR-100 and Tiny-ImageNet. We further show that robust classifiers vary in the intensity and quality of their generative capabilities, and offer a simple method to push this capability, reaching a remarkable Inception Score (IS) and FID using a robust classifier without training for generative modeling. The code to reproduce our results is available at http://github.com/OmnAI-Lab/Robust-Classifiers-under-the-lens-of-EBM/ .
|
http://arxiv.org/pdf/2407.06315v2
|
[
"Mujtaba Hussain Mirza",
"Maria Rosaria Briglia",
"Senad Beadini",
"Iacopo Masi"
] |
2024-07-11T11:11:03Z
|
2024-07-08T18:31:19Z
|
2407.08364
|
Scalar Function Topology Divergence: Comparing Topology of 3D Objects
|
We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets of two functions having a common domain. Functions can be defined on an undirected graph or Euclidean space of any dimensionality. Most of the existing methods for comparing topology are based on Wasserstein distance between persistence barcodes and they don't take into account the localization of topological features. On the other hand, the minimization of SFTD ensures that the corresponding topological features of scalar functions are located in the same places. The proposed tool provides useful visualizations depicting areas where functions have topological dissimilarities. We provide applications of the proposed method to 3D computer vision. In particular, experiments demonstrate that SFTD improves the reconstruction of cellular 3D shapes from 2D fluorescence microscopy images, and helps to identify topological errors in 3D segmentation.
|
http://arxiv.org/pdf/2407.08364v1
|
[
"Ilya Trofimov",
"Daria Voronkova",
"Eduard Tulchinskii",
"Evgeny Burnaev",
"Serguei Barannikov"
] |
2024-07-11T10:18:54Z
|
2024-07-11T10:18:54Z
|
2306.02271
|
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
|
Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and Root-MUSIC, rely on several restrictive assumptions, including narrowband non-coherent sources and fully calibrated arrays, and their performance is considerably degraded when these do not hold. In this work we propose SubspaceNet; a data-driven DoA estimator which learns how to divide the observations into distinguishable subspaces. This is achieved by utilizing a dedicated deep neural network to learn the empirical autocorrelation of the input, by training it as part of the Root-MUSIC method, leveraging the inherent differentiability of this specific DoA estimator, while removing the need to provide a ground-truth decomposable autocorrelation matrix. Once trained, the resulting SubspaceNet serves as a universal surrogate covariance estimator that can be applied in combination with any subspace-based DoA estimation method, allowing its successful application in challenging setups. SubspaceNet is shown to enable various DoA estimation algorithms to cope with coherent sources, wideband signals, low SNR, array mismatches, and limited snapshots, while preserving the interpretability and the suitability of classic subspace methods.
|
http://arxiv.org/pdf/2306.02271v2
|
[
"Dor H. Shmuel",
"Julian P. Merkofer",
"Guy Revach",
"Ruud J. G. van Sloun",
"Nir Shlezinger"
] |
2024-07-11T10:16:41Z
|
2023-06-04T06:30:13Z
|
2302.13939
|
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural
Networks
|
As the size of large language models continue to scale, so does the computational resources required to run it. Spiking Neural Networks (SNNs) have emerged as an energy-efficient approach to deep learning that leverage sparse and event-driven activations to reduce the computational overhead associated with model inference. While they have become competitive with non-spiking models on many computer vision tasks, SNNs have also proven to be more challenging to train. As a result, their performance lags behind modern deep learning, and we are yet to see the effectiveness of SNNs in language generation. In this paper, inspired by the Receptance Weighted Key Value (RWKV) language model, we successfully implement `SpikeGPT', a generative language model with binary, event-driven spiking activation units. We train the proposed model on two model variants: 45M and 216M parameters. To the best of our knowledge, SpikeGPT is the largest backpropagation-trained SNN model to date, rendering it suitable for both the generation and comprehension of natural language. We achieve this by modifying the transformer block to replace multi-head self attention to reduce quadratic computational complexity O(N^2) to linear complexity O(N) with increasing sequence length. Input tokens are instead streamed in sequentially to our attention mechanism (as with typical SNNs). Our preliminary experiments show that SpikeGPT remains competitive with non-spiking models on tested benchmarks, while maintaining 20x fewer operations when processed on neuromorphic hardware that can leverage sparse, event-driven activations. Our code implementation is available at https://github.com/ridgerchu/SpikeGPT.
|
http://arxiv.org/pdf/2302.13939v5
|
[
"Rui-Jie Zhu",
"Qihang Zhao",
"Guoqi Li",
"Jason K. Eshraghian"
] |
2024-07-11T10:16:12Z
|
2023-02-27T16:43:04Z
|
2407.08362
|
STAL: Spike Threshold Adaptive Learning Encoder for Classification of
Pain-Related Biosignal Data
|
This paper presents the first application of spiking neural networks (SNNs) for the classification of chronic lower back pain (CLBP) using the EmoPain dataset. Our work has two main contributions. We introduce Spike Threshold Adaptive Learning (STAL), a trainable encoder that effectively converts continuous biosignals into spike trains. Additionally, we propose an ensemble of Spiking Recurrent Neural Network (SRNN) classifiers for the multi-stream processing of sEMG and IMU data. To tackle the challenges of small sample size and class imbalance, we implement minority over-sampling with weighted sample replacement during batch creation. Our method achieves outstanding performance with an accuracy of 80.43%, AUC of 67.90%, F1 score of 52.60%, and Matthews Correlation Coefficient (MCC) of 0.437, surpassing traditional rate-based and latency-based encoding methods. The STAL encoder shows superior performance in preserving temporal dynamics and adapting to signal characteristics. Importantly, our approach (STAL-SRNN) outperforms the best deep learning method in terms of MCC, indicating better balanced class prediction. This research contributes to the development of neuromorphic computing for biosignal analysis. It holds promise for energy-efficient, wearable solutions in chronic pain management.
|
http://arxiv.org/pdf/2407.08362v1
|
[
"Freek Hens",
"Mohammad Mahdi Dehshibi",
"Leila Bagheriye",
"Mahyar Shahsavari",
"Ana Tajadura-Jiménez"
] |
2024-07-11T10:15:52Z
|
2024-07-11T10:15:52Z
|
2407.08351
|
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language
Models
|
Evaluation is critical for assessing capabilities, tracking scientific progress, and informing model selection. In this paper, we present three desiderata for a good benchmark for language models: (i) salience (e.g., knowledge about World War II is more salient than a random day in history), (ii) novelty (i.e., the benchmark reveals new trends in model rankings not shown by previous benchmarks), and (iii) difficulty (i.e., the benchmark should be difficult for existing models, leaving headroom for future improvement). We operationalize these three desiderata and cast benchmark creation as a search problem, that of finding benchmarks that that satisfy all three desiderata. To tackle this search problem, we present AutoBencher, which uses a language model to automatically search for datasets that meet the three desiderata. AutoBencher uses privileged information (e.g. relevant documents) to construct reliable datasets, and adaptivity with reranking to optimize for the search objective. We use AutoBencher to create datasets for math, multilingual, and knowledge-intensive question answering. The scalability of AutoBencher allows it to test fine-grained categories and tail knowledge, creating datasets that are on average 27% more novel and 22% more difficult than existing benchmarks. A closer investigation of our constructed datasets shows that we can identify specific gaps in LM knowledge in language models that are not captured by existing benchmarks, such as Gemini Pro performing much worse on question answering about the Permian Extinction and Fordism, while OpenAGI-7B performing surprisingly well on QA about COVID-19.
|
http://arxiv.org/pdf/2407.08351v1
|
[
"Xiang Lisa Li",
"Evan Zheran Liu",
"Percy Liang",
"Tatsunori Hashimoto"
] |
2024-07-11T10:03:47Z
|
2024-07-11T10:03:47Z
|
2407.08348
|
Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large
Language Models -- The Story Goes On
|
In this paper, we investigate the underlying factors that potentially enhance the mathematical reasoning capabilities of large language models (LLMs). We argue that the data scaling law for math reasoning capabilities in modern LLMs is far from being saturated, highlighting how the model's quality improves with increases in data quantity. To support this claim, we introduce the Skywork-Math model series, supervised fine-tuned (SFT) on common 7B LLMs using our proposed 2.5M-instance Skywork-MathQA dataset. Skywork-Math 7B has achieved impressive accuracies of 51.2% on the competition-level MATH benchmark and 83.9% on the GSM8K benchmark using only SFT data, outperforming an early version of GPT-4 on MATH. The superior performance of Skywork-Math models contributes to our novel two-stage data synthesis and model SFT pipelines, which include three different augmentation methods and a diverse seed problem set, ensuring both the quantity and quality of Skywork-MathQA dataset across varying difficulty levels. Most importantly, we provide several practical takeaways to enhance math reasoning abilities in LLMs for both research and industry applications.
|
http://arxiv.org/pdf/2407.08348v1
|
[
"Liang Zeng",
"Liangjun Zhong",
"Liang Zhao",
"Tianwen Wei",
"Liu Yang",
"Jujie He",
"Cheng Cheng",
"Rui Hu",
"Yang Liu",
"Shuicheng Yan",
"Han Fang",
"Yahui Zhou"
] |
2024-07-11T09:56:51Z
|
2024-07-11T09:56:51Z
|
2407.08340
|
SLRL: Structured Latent Representation Learning for Multi-view
Clustering
|
In recent years, Multi-View Clustering (MVC) has attracted increasing attention for its potential to reduce the annotation burden associated with large datasets. The aim of MVC is to exploit the inherent consistency and complementarity among different views, thereby integrating information from multiple perspectives to improve clustering outcomes. Despite extensive research in MVC, most existing methods focus predominantly on harnessing complementary information across views to enhance clustering effectiveness, often neglecting the structural information among samples, which is crucial for exploring sample correlations. To address this gap, we introduce a novel framework, termed Structured Latent Representation Learning based Multi-View Clustering method (SLRL). SLRL leverages both the complementary and structural information. Initially, it learns a common latent representation for all views. Subsequently, to exploit the structural information among samples, a k-nearest neighbor graph is constructed from this common latent representation. This graph facilitates enhanced sample interaction through graph learning techniques, leading to a structured latent representation optimized for clustering. Extensive experiments demonstrate that SLRL not only competes well with existing methods but also sets new benchmarks in various multi-view datasets.
|
http://arxiv.org/pdf/2407.08340v1
|
[
"Zhangci Xiong",
"Meng Cao"
] |
2024-07-11T09:43:57Z
|
2024-07-11T09:43:57Z
|
2407.08337
|
FedLog: Personalized Federated Classification with Less Communication
and More Flexibility
|
In federated learning (FL), the common paradigm that FedAvg proposes and most algorithms follow is that clients train local models with their private data, and the model parameters are shared for central aggregation, mostly averaging. In this paradigm, the communication cost is often a challenge, as modern massive neural networks can contain millions to billions parameters. We suggest that clients do not share model parameters but local data summaries, to decrease the cost of sharing. We develop a new algorithm FedLog with Bayesian inference, which shares only sufficient statistics of local data. FedLog transmits messages as small as the last layer of the original model. We conducted comprehensive experiments to show we outperform other FL algorithms that aim at decreasing the communication cost. To provide formal privacy guarantees, we further extend FedLog with differential privacy and show the trade-off between privacy budget and accuracy.
|
http://arxiv.org/pdf/2407.08337v1
|
[
"Haolin Yu",
"Guojun Zhang",
"Pascal Poupart"
] |
2024-07-11T09:40:29Z
|
2024-07-11T09:40:29Z
|
2005.14105
|
Provably Good Solutions to the Knapsack Problem via Neural Networks of
Bounded Size
|
The development of a satisfying and rigorous mathematical understanding of the performance of neural networks is a major challenge in artificial intelligence. Against this background, we study the expressive power of neural networks through the example of the classical NP-hard Knapsack Problem. Our main contribution is a class of recurrent neural networks (RNNs) with rectified linear units that are iteratively applied to each item of a Knapsack instance and thereby compute optimal or provably good solution values. We show that an RNN of depth four and width depending quadratically on the profit of an optimum Knapsack solution is sufficient to find optimum Knapsack solutions. We also prove the following tradeoff between the size of an RNN and the quality of the computed Knapsack solution: for Knapsack instances consisting of $n$ items, an RNN of depth five and width $w$ computes a solution of value at least $1-mathcal{O}(n^2/sqrt{w})$ times the optimum solution value. Our results build upon a classical dynamic programming formulation of the Knapsack Problem as well as a careful rounding of profit values that are also at the core of the well-known fully polynomial-time approximation scheme for the Knapsack Problem. A carefully conducted computational study qualitatively supports our theoretical size bounds. Finally, we point out that our results can be generalized to many other combinatorial optimization problems that admit dynamic programming solution methods, such as various Shortest Path Problems, the Longest Common Subsequence Problem, and the Traveling Salesperson Problem.
|
http://arxiv.org/abs/2005.14105v3
|
[
"Christoph Hertrich",
"Martin Skutella"
] |
2024-07-11T09:39:30Z
|
2020-05-28T15:55:37Z
|
2312.01648
|
Characterizing Large Language Model Geometry Helps Solve Toxicity
Detection and Generation
|
Large Language Models (LLMs) drive current AI breakthroughs despite very little being known about their internal representations. In this work, we propose to shed the light on LLMs inner mechanisms through the lens of geometry. In particular, we develop in closed form $(i)$ the intrinsic dimension in which the Multi-Head Attention embeddings are constrained to exist and $(ii)$ the partition and per-region affine mappings of the feedforward (MLP) network of LLMs' layers. Our theoretical findings further enable the design of novel principled solutions applicable to state-of-the-art LLMs. First, we show that, through our geometric understanding, we can bypass LLMs' RLHF protection by controlling the embedding's intrinsic dimension through informed prompt manipulation. Second, we derive interpretable geometrical features that can be extracted from any (pre-trained) LLM, providing a rich abstract representation of their inputs. We observe that these features are sufficient to help solve toxicity detection, and even allow the identification of various types of toxicity. Our results demonstrate how, even in large-scale regimes, exact theoretical results can answer practical questions in LLMs. Code: https://github.com/RandallBalestriero/SplineLLM
|
http://arxiv.org/pdf/2312.01648v3
|
[
"Randall Balestriero",
"Romain Cosentino",
"Sarath Shekkizhar"
] |
2024-07-11T09:32:19Z
|
2023-12-04T06:01:32Z
|
2407.08330
|
HDT: Hierarchical Document Transformer
|
In this paper, we propose the Hierarchical Document Transformer (HDT), a novel sparse Transformer architecture tailored for structured hierarchical documents. Such documents are extremely important in numerous domains, including science, law or medicine. However, most existing solutions are inefficient and fail to make use of the structure inherent to documents. HDT exploits document structure by introducing auxiliary anchor tokens and redesigning the attention mechanism into a sparse multi-level hierarchy. This approach facilitates information exchange between tokens at different levels while maintaining sparsity, thereby enhancing computational and memory efficiency while exploiting the document structure as an inductive bias. We address the technical challenge of implementing HDT's sample-dependent hierarchical attention pattern by developing a novel sparse attention kernel that considers the hierarchical structure of documents. As demonstrated by our experiments, utilizing structural information present in documents leads to faster convergence, higher sample efficiency and better performance on downstream tasks.
|
http://arxiv.org/pdf/2407.08330v1
|
[
"Haoyu He",
"Markus Flicke",
"Jan Buchmann",
"Iryna Gurevych",
"Andreas Geiger"
] |
2024-07-11T09:28:04Z
|
2024-07-11T09:28:04Z
|
2407.08328
|
Unveiling Disparities in Maternity Care: A Topic Modelling Approach to
Analysing Maternity Incident Investigation Reports
|
This study applies Natural Language Processing techniques, including Latent Dirichlet Allocation, to analyse anonymised maternity incident investigation reports from the Healthcare Safety Investigation Branch. The reports underwent preprocessing, annotation using the Safety Intelligence Research taxonomy, and topic modelling to uncover prevalent topics and detect differences in maternity care across ethnic groups. A combination of offline and online methods was utilised to ensure data protection whilst enabling advanced analysis, with offline processing for sensitive data and online processing for non-sensitive data using the `Claude 3 Opus' language model. Interactive topic analysis and semantic network visualisation were employed to extract and display thematic topics and visualise semantic relationships among keywords. The analysis revealed disparities in care among different ethnic groups, with distinct focus areas for the Black, Asian, and White British ethnic groups. The study demonstrates the effectiveness of topic modelling and NLP techniques in analysing maternity incident investigation reports and highlighting disparities in care. The findings emphasise the crucial role of advanced data analysis in improving maternity care quality and equity.
|
http://arxiv.org/pdf/2407.08328v1
|
[
"Georgina Cosma",
"Mohit Kumar Singh",
"Patrick Waterson",
"Gyuchan Thomas Jun",
"Jonathan Back"
] |
2024-07-11T09:26:05Z
|
2024-07-11T09:26:05Z
|
2407.03133
|
Quantifying the Cross-sectoral Intersecting Discrepancies within
Multiple Groups Using Latent Class Analysis Towards Fairness
|
The growing interest in fair AI development is evident. The ''Leave No One Behind'' initiative urges us to address multiple and intersecting forms of inequality in accessing services, resources, and opportunities, emphasising the significance of fairness in AI. This is particularly relevant as an increasing number of AI tools are applied to decision-making processes, such as resource allocation and service scheme development, across various sectors such as health, energy, and housing. Therefore, exploring joint inequalities in these sectors is significant and valuable for thoroughly understanding overall inequality and unfairness. This research introduces an innovative approach to quantify cross-sectoral intersecting discrepancies among user-defined groups using latent class analysis. These discrepancies can be used to approximate inequality and provide valuable insights to fairness issues. We validate our approach using both proprietary and public datasets, including EVENS and Census 2021 (England & Wales) datasets, to examine cross-sectoral intersecting discrepancies among different ethnic groups. We also verify the reliability of the quantified discrepancy by conducting a correlation analysis with a government public metric. Our findings reveal significant discrepancies between minority ethnic groups, highlighting the need for targeted interventions in real-world AI applications. Additionally, we demonstrate how the proposed approach can be used to provide insights into the fairness of machine learning.
|
http://arxiv.org/pdf/2407.03133v2
|
[
"Yingfang Yuan",
"Kefan Chen",
"Mehdi Rizvi",
"Lynne Baillie",
"Wei Pang"
] |
2024-07-11T09:19:11Z
|
2024-05-24T08:10:31Z
|
2407.08324
|
A Cantor-Kantorovich Metric Between Markov Decision Processes with
Application to Transfer Learning
|
We extend the notion of Cantor-Kantorovich distance between Markov chains introduced by (Banse et al., 2023) in the context of Markov Decision Processes (MDPs). The proposed metric is well-defined and can be efficiently approximated given a finite horizon. Then, we provide numerical evidences that the latter metric can lead to interesting applications in the field of reinforcement learning. In particular, we show that it could be used for forecasting the performance of transfer learning algorithms.
|
http://arxiv.org/pdf/2407.08324v1
|
[
"Adrien Banse",
"Venkatraman Renganathan",
"Raphaël M. Jungers"
] |
2024-07-11T09:13:11Z
|
2024-07-11T09:13:11Z
|
2403.03881
|
Latent Dataset Distillation with Diffusion Models
|
Machine learning traditionally relies on increasingly larger datasets. Yet, such datasets pose major storage challenges and usually contain non-influential samples, which could be ignored during training without negatively impacting the training quality. In response, the idea of distilling a dataset into a condensed set of synthetic samples, i.e., a distilled dataset, emerged. One key aspect is the selected architecture, usually ConvNet, for linking the original and synthetic datasets. However, the final accuracy is lower if the employed model architecture differs from that used during distillation. Another challenge is the generation of high-resolution images (128x128 and higher). To address both challenges, this paper proposes Latent Dataset Distillation with Diffusion Models (LD3M) that combine diffusion in latent space with dataset distillation. Our novel diffusion process is tailored for this task and significantly improves the gradient flow for distillation. By adjusting the number of diffusion steps, LD3M also offers a convenient way of controlling the trade-off between distillation speed and dataset quality. Overall, LD3M consistently outperforms state-of-the-art methods by up to 4.8 p.p. and 4.2 p.p. for 1 and 10 images per class, respectively, and on several ImageNet subsets and high resolutions (128x128 and 256x256).
|
http://arxiv.org/pdf/2403.03881v3
|
[
"Brian B. Moser",
"Federico Raue",
"Sebastian Palacio",
"Stanislav Frolov",
"Andreas Dengel"
] |
2024-07-11T09:10:10Z
|
2024-03-06T17:41:41Z
|
2407.05832
|
A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar
Surface
|
This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The research leverages diverse planetary datasets, including high-spatial-resolution albedo maps and element maps (LPFe, LPK, LPTh, LPTi) derived from laser and gamma-ray measurements. The primary objective is to identify relationships between chemical elements and albedo, thereby expanding our understanding of planetary surfaces and offering predictive capabilities for areas with incomplete datasets. To bridge the gap in resolution between the albedo and element maps, we employ Gaussian blurring techniques, including an innovative adaptive Gaussian blur. Our methodology culminates in the deployment of an Extreme Gradient Boosting Regression Model, optimized to predict full albedo based on elemental composition. Furthermore, we present an interactive analytical tool to visualize prediction errors, delineating their spatial and chemical characteristics. The findings not only pave the way for a more comprehensive understanding of the Moon's surface but also provide a framework for similar studies on other celestial bodies.
|
http://arxiv.org/pdf/2407.05832v2
|
[
"Sofia Strukova",
"Sergei Gleyzer",
"Patrick Peplowski",
"Jason P. Terry"
] |
2024-07-11T09:10:09Z
|
2024-07-08T11:25:30Z
|
2407.08316
|
Enhancing ADHD Diagnosis with EEG: The Critical Role of Preprocessing
and Key Features
|
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly impacts various key aspects of life, requiring accurate diagnostic methods. Electroencephalogram (EEG) signals are used in diagnosing ADHD, but proper preprocessing is crucial to avoid noise and artifacts that could lead to unreliable results. Method: This study utilized a public EEG dataset from children diagnosed with ADHD and typically developing (TD) children. Four preprocessing techniques were applied: no preprocessing (Raw), Finite Impulse Response (FIR) filtering, Artifact Subspace Reconstruction (ASR), and Independent Component Analysis (ICA). EEG recordings were segmented, and features were extracted and selected based on statistical significance. Classification was performed using Machine Learning models, as XGBoost, Support Vector Machine, and K-Nearest Neighbors. Results: The absence of preprocessing leads to artificially high classification accuracy due to noise. In contrast, ASR and ICA preprocessing techniques significantly improved the reliability of results. Segmenting EEG recordings revealed that later segments provided better classification accuracy, likely due to the manifestation of ADHD symptoms over time. The most relevant EEG channels were P3, P4, and C3. The top features for classification included Kurtosis, Katz fractal dimension, and power spectral density of Delta, Theta, and Alpha bands. Conclusions: Effective preprocessing is essential in EEG-based ADHD diagnosis to prevent noise-induced biases. This study identifies crucial EEG channels and features, providing a foundation for further research and improving ADHD diagnostic accuracy. Future work should focus on expanding datasets, refining preprocessing methods, and enhancing feature interpretability to improve diagnostic accuracy and model robustness for clinical use.
|
http://arxiv.org/pdf/2407.08316v1
|
[
"Sandra García-Ponsoda",
"Alejandro Maté",
"Juan Trujillo"
] |
2024-07-11T09:07:22Z
|
2024-07-11T09:07:22Z
|
2407.08313
|
Improving Molecular Modeling with Geometric GNNs: an Empirical Study
|
Rapid advancements in machine learning (ML) are transforming materials science by significantly speeding up material property calculations. However, the proliferation of ML approaches has made it challenging for scientists to keep up with the most promising techniques. This paper presents an empirical study on Geometric Graph Neural Networks for 3D atomic systems, focusing on the impact of different (1) canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks.
|
http://arxiv.org/pdf/2407.08313v1
|
[
"Ali Ramlaoui",
"Théo Saulus",
"Basile Terver",
"Victor Schmidt",
"David Rolnick",
"Fragkiskos D. Malliaros",
"Alexandre Duval"
] |
2024-07-11T09:04:12Z
|
2024-07-11T09:04:12Z
|
2405.14908
|
Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model
Pretraining
|
Large language models exhibit exceptional generalization capabilities, primarily attributed to the utilization of diversely sourced data. However, conventional practices in integrating this diverse data heavily rely on heuristic schemes, lacking theoretical guidance. This research tackles these limitations by investigating strategies based on low-cost proxies for data mixtures, with the aim of streamlining data curation to enhance training efficiency. Specifically, we propose a unified scaling law, termed $textbf{BiMix}$, which accurately models the bivariate scaling behaviors of both data quantity and mixing proportions. We conduct systematic experiments and provide empirical evidence for the predictive power and fundamental principles of $textbf{BiMix}$. Notably, our findings reveal that entropy-driven training-free data mixtures can achieve comparable or even better performance than more resource-intensive methods. We hope that our quantitative insights can shed light on further judicious research and development in cost-effective language modeling.
|
http://arxiv.org/pdf/2405.14908v2
|
[
"Ce Ge",
"Zhijian Ma",
"Daoyuan Chen",
"Yaliang Li",
"Bolin Ding"
] |
2024-07-11T08:44:45Z
|
2024-05-23T09:44:02Z
|
2407.08298
|
XAI-Guided Enhancement of Vegetation Indices for Crop Mapping
|
Vegetation indices allow to efficiently monitor vegetation growth and agricultural activities. Previous generations of satellites were capturing a limited number of spectral bands, and a few expert-designed vegetation indices were sufficient to harness their potential. New generations of multi- and hyperspectral satellites can however capture additional bands, but are not yet efficiently exploited. In this work, we propose an explainable-AI-based method to select and design suitable vegetation indices. We first train a deep neural network using multispectral satellite data, then extract feature importance to identify the most influential bands. We subsequently select suitable existing vegetation indices or modify them to incorporate the identified bands and retrain our model. We validate our approach on a crop classification task. Our results indicate that models trained on individual indices achieve comparable results to the baseline model trained on all bands, while the combination of two indices surpasses the baseline in certain cases.
|
http://arxiv.org/pdf/2407.08298v1
|
[
"Hiba Najjar",
"Francisco Mena",
"Marlon Nuske",
"Andreas Dengel"
] |
2024-07-11T08:44:43Z
|
2024-07-11T08:44:43Z
|
2407.08296
|
Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive
Low-Rank Gradients
|
Training Large Language Models (LLMs) is memory-intensive due to the large number of parameters and associated optimization states. GaLore, a recent method, reduces memory usage by projecting weight gradients into a low-rank subspace without compromising performance. However, GaLore relies on time-consuming Singular Value Decomposition (SVD) operations to identify the subspace, and the frequent subspace updates lead to significant training time overhead. Moreover, GaLore offers minimal improvements in accuracy and efficiency compared to LoRA in more accessible fine-tuning scenarios. To address these limitations, we introduce Q-Galore, a novel approach that substantially reduces memory usage by combining quantization and low-rank projection, surpassing the benefits of GaLore. Our method is based on two key observations: (i) the gradient subspace exhibits diverse properties, with some layers converging early in training while others are subject to frequent changes; (ii) the projection matrices are highly resilient to low-bit quantization. Leveraging these insights, Q-GaLore adaptively updates the gradient subspace based on its convergence statistics, achieving comparable performance while significantly reducing the number of SVD operations. We maintain the projection matrices in INT4 format and weights in INT8 format, incorporating stochastic rounding to capture accumulated gradient information. This approach enables a high-precision training trajectory using only low-precision weights. We demonstrate that Q-GaLore achieves highly competitive performance with exceptional memory efficiency. At pre-training, Q-GaLore facilitates training a LLaMA-7B model from scratch on a single NVIDIA RTX 4060 Ti with only 16 GB memory. At fine-tuning, it reduces memory consumption by up to 50% compared to LoRA and GaLore, while consistently outperforming QLoRA at the same memory cost.
|
http://arxiv.org/pdf/2407.08296v1
|
[
"Zhenyu Zhang",
"Ajay Jaiswal",
"Lu Yin",
"Shiwei Liu",
"Jiawei Zhao",
"Yuandong Tian",
"Zhangyang Wang"
] |
2024-07-11T08:42:58Z
|
2024-07-11T08:42:58Z
|
2407.08289
|
Predicting Heart Failure with Attention Learning Techniques Utilizing
Cardiovascular Data
|
Cardiovascular diseases (CVDs) encompass a group of disorders affecting the heart and blood vessels, including conditions such as coronary artery disease, heart failure, stroke, and hypertension. In cardiovascular diseases, heart failure is one of the main causes of death and also long-term suffering in patients worldwide. Prediction is one of the risk factors that is highly valuable for treatment and intervention to minimize heart failure. In this work, an attention learning-based heart failure prediction approach is proposed on EHR(electronic health record) cardiovascular data such as ejection fraction and serum creatinine. Moreover, different optimizers with various learning rate approaches are applied to fine-tune the proposed approach. Serum creatinine and ejection fraction are the two most important features to predict the patient's heart failure. The computational result shows that the RMSProp optimizer with 0.001 learning rate has a better prediction based on serum creatinine. On the other hand, the combination of SGD optimizer with 0.01 learning rate exhibits optimum performance based on ejection fraction features. Overall, the proposed attention learning-based approach performs very efficiently in predicting heart failure compared to the existing state-of-the-art such as LSTM approach.
|
http://arxiv.org/pdf/2407.08289v1
|
[
"Ershadul Haque",
"Manoranjan Paul",
"Faranak Tohidi"
] |
2024-07-11T08:33:42Z
|
2024-07-11T08:33:42Z
|
2407.08282
|
AoA-Based Physical Layer Authentication in Analog Arrays under
Impersonation Attacks
|
We discuss the use of angle of arrival (AoA) as an authentication measure in analog array multiple-input multiple-output (MIMO) systems. A base station equipped with an analog array authenticates users based on the AoA estimated from certified pilot transmissions, while active attackers manipulate their transmitted signals to mount impersonation attacks. We study several attacks of increasing intensity (captured through the availability of side information at the attackers) and assess the performance of AoA-based authentication using one-class classifiers. Our results show that some attack techniques with knowledge of the combiners at the verifier are effective in falsifying the AoA and compromising the security of the considered type of physical layer authentication.
|
http://arxiv.org/pdf/2407.08282v1
|
[
"Muralikrishnan Srinivasan",
"Linda Senigagliesi",
"Hui Chen",
"Arsenia Chorti",
"Marco Baldi",
"Henk Wymeersch"
] |
2024-07-11T08:30:04Z
|
2024-07-11T08:30:04Z
|
2407.08274
|
Explainability of Sub-Field Level Crop Yield Prediction using Remote
Sensing
|
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and decision-making processes of the models, as well as their interaction with the input data, is crucial for establishing trust in the models and gaining insight into their reliability. In this study, we focus on the task of crop yield prediction, specifically for soybean, wheat, and rapeseed crops in Argentina, Uruguay, and Germany. Our goal is to develop and explain predictive models for these crops, using a large dataset of satellite images, additional data modalities, and crop yield maps. We employ a long short-term memory network and investigate the impact of using different temporal samplings of the satellite data and the benefit of adding more relevant modalities. For model explainability, we utilize feature attribution methods to quantify input feature contributions, identify critical growth stages, analyze yield variability at the field level, and explain less accurate predictions. The modeling results show an improvement when adding more modalities or using all available instances of satellite data. The explainability results reveal distinct feature importance patterns for each crop and region. We further found that the most influential growth stages on the prediction are dependent on the temporal sampling of the input data. We demonstrated how these critical growth stages, which hold significant agronomic value, closely align with the existing literature in agronomy and crop development biology.
|
http://arxiv.org/pdf/2407.08274v1
|
[
"Hiba Najjar",
"Miro Miranda",
"Marlon Nuske",
"Ribana Roscher",
"Andreas Dengel"
] |
2024-07-11T08:23:46Z
|
2024-07-11T08:23:46Z
|
2407.08271
|
Gaussian process interpolation with conformal prediction: methods and
comparative analysis
|
This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration of prediction intervals. We begin by illustrating that using a GP model with parameters selected by maximum likelihood often results in predictions that are not optimally calibrated. CP methods can adjust the prediction intervals, leading to better uncertainty quantification while maintaining the accuracy of the underlying GP model. We compare different CP variants and introduce a novel variant based on an asymmetric score. Our numerical experiments demonstrate the effectiveness of CP methods in improving calibration without compromising accuracy. This work aims to facilitate the adoption of CP methods in the GP community.
|
http://arxiv.org/pdf/2407.08271v1
|
[
"Aurélien Pion",
"Emmanuel Vazquez"
] |
2024-07-11T08:15:57Z
|
2024-07-11T08:15:57Z
|
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