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2407.07291
|
Causal Discovery in Semi-Stationary Time Series
|
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas, such as retail sales, transportation systems, and medical science. Here, we consider this problem for a class of non-stationary time series. The structural causal model (SCM) of this type of time series, called the semi-stationary time series, exhibits that a finite number of different causal mechanisms occur sequentially and periodically across time. This model holds considerable practical utility because it can represent periodicity, including common occurrences such as seasonality and diurnal variation. We propose a constraint-based, non-parametric algorithm for discovering causal relations in this setting. The resulting algorithm, PCMCI$_{Omega}$, can capture the alternating and recurring changes in the causal mechanisms and then identify the underlying causal graph with conditional independence (CI) tests. We show that this algorithm is sound in identifying causal relations on discrete time series. We validate the algorithm with extensive experiments on continuous and discrete simulated data. We also apply our algorithm to a real-world climate dataset.
|
http://arxiv.org/pdf/2407.07291v1
|
[
"Shanyun Gao",
"Raghavendra Addanki",
"Tong Yu",
"Ryan A. Rossi",
"Murat Kocaoglu"
] |
2024-07-10T00:55:38Z
|
2024-07-10T00:55:38Z
|
2407.07290
|
Causal Discovery-Driven Change Point Detection in Time Series
|
Change point detection in time series seeks to identify times when the probability distribution of time series changes. It is widely applied in many areas, such as human-activity sensing and medical science. In the context of multivariate time series, this typically involves examining the joint distribution of high-dimensional data: If any one variable changes, the whole time series is assumed to have changed. However, in practical applications, we may be interested only in certain components of the time series, exploring abrupt changes in their distributions in the presence of other time series. Here, assuming an underlying structural causal model that governs the time-series data generation, we address this problem by proposing a two-stage non-parametric algorithm that first learns parts of the causal structure through constraint-based discovery methods. The algorithm then uses conditional relative Pearson divergence estimation to identify the change points. The conditional relative Pearson divergence quantifies the distribution disparity between consecutive segments in the time series, while the causal discovery method enables a focus on the causal mechanism, facilitating access to independent and identically distributed (IID) samples. Theoretically, the typical assumption of samples being IID in conventional change point detection methods can be relaxed based on the Causal Markov Condition. Through experiments on both synthetic and real-world datasets, we validate the correctness and utility of our approach.
|
http://arxiv.org/pdf/2407.07290v1
|
[
"Shanyun Gao",
"Raghavendra Addanki",
"Tong Yu",
"Ryan A. Rossi",
"Murat Kocaoglu"
] |
2024-07-10T00:54:42Z
|
2024-07-10T00:54:42Z
|
2312.14439
|
PUMA: Efficient Continual Graph Learning for Node Classification with
Graph Condensation
|
When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is easily overwritten when learning with newly incoming graphs. In response, Continual Graph Learning (CGL) emerges as a novel paradigm enabling graph representation learning from streaming graphs. Our prior work, Condense and Train (CaT) is a replay-based CGL framework with a balanced continual learning procedure, which designs a small yet effective memory bankn for replaying. Although the CaT alleviates the catastrophic forgetting problem, there exist three issues: (1) The graph condensation only focuses on labelled nodes while neglecting abundant information carried by unlabelled nodes; (2) The continual training scheme of the CaT overemphasises on the previously learned knowledge, limiting the model capacity to learn from newly added memories; (3) Both the condensation process and replaying process of the CaT are time-consuming. In this paper, we propose a PsUdo-label guided Memory bAnk (PUMA) CGL framework, extending from the CaT to enhance its efficiency and effectiveness by overcoming the above-mentioned weaknesses and limits. To fully exploit the information in a graph, PUMA expands the coverage of nodes during graph condensation with both labelled and unlabelled nodes. Furthermore, a training-from-scratch strategy is proposed to upgrade the previous continual learning scheme for a balanced training between the historical and the new graphs. Besides, PUMA uses a one-time prorogation and wide graph encoders to accelerate the graph condensation and the graph encoding process in the training stage to improve the efficiency of the whole framework. Extensive experiments on six datasets for the node classification task demonstrate the state-of-the-art performance and efficiency over existing methods.
|
http://arxiv.org/pdf/2312.14439v2
|
[
"Yilun Liu",
"Ruihong Qiu",
"Yanran Tang",
"Hongzhi Yin",
"Zi Huang"
] |
2024-07-10T00:37:02Z
|
2023-12-22T05:09:58Z
|
2311.12917
|
Orchard: building large cancer phylogenies using stochastic
combinatorial search
|
Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations.
|
http://arxiv.org/pdf/2311.12917v2
|
[
"E. Kulman",
"R. Kuang",
"Q. Morris"
] |
2024-07-10T00:32:49Z
|
2023-11-21T18:25:23Z
|
2407.07279
|
Towards a theory of learning dynamics in deep state space models
|
State space models (SSMs) have shown remarkable empirical performance on many long sequence modeling tasks, but a theoretical understanding of these models is still lacking. In this work, we study the learning dynamics of linear SSMs to understand how covariance structure in data, latent state size, and initialization affect the evolution of parameters throughout learning with gradient descent. We show that focusing on the learning dynamics in the frequency domain affords analytical solutions under mild assumptions, and we establish a link between one-dimensional SSMs and the dynamics of deep linear feed-forward networks. Finally, we analyze how latent state over-parameterization affects convergence time and describe future work in extending our results to the study of deep SSMs with nonlinear connections. This work is a step toward a theory of learning dynamics in deep state space models.
|
http://arxiv.org/pdf/2407.07279v1
|
[
"Jakub Smékal",
"Jimmy T. H. Smith",
"Michael Kleinman",
"Dan Biderman",
"Scott W. Linderman"
] |
2024-07-10T00:01:56Z
|
2024-07-10T00:01:56Z
|
2407.07277
|
Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel
Representation Learning
|
Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Current reference values and recommended ranges often rely on population-level statistics, which may not adequately account for the influence of inter-individual variability driven by factors such as lifestyle and genetics. In this work, we introduce a novel framework for predicting future blood biomarker values and define personalized references through learned representations from lifestyle data (physical activity and sleep) and blood biomarkers. Our proposed method learns a similarity-based embedding space that captures the complex relationship between biomarkers and lifestyle factors. Using the UK Biobank (257K participants), our results show that our deep-learned embeddings outperform traditional and current state-of-the-art representation learning techniques in predicting clinical diagnosis. Using a subset of UK Biobank of 6440 participants who have follow-up visits, we validate that the inclusion of these embeddings and lifestyle factors directly in blood biomarker models improves the prediction of future lab values from a single lab visit. This personalized modeling approach provides a foundation for developing more accurate risk stratification tools and tailoring preventative care strategies. In clinical settings, this translates to the potential for earlier disease detection, more timely interventions, and ultimately, a shift towards personalized healthcare.
|
http://arxiv.org/pdf/2407.07277v1
|
[
"A. Ali Heydari",
"Naghmeh Rezaei",
"Javier L. Prieto",
"Shwetak N. Patel",
"Ahmed A. Metwally"
] |
2024-07-09T23:52:53Z
|
2024-07-09T23:52:53Z
|
2312.03404
|
A cyclical route linking fundamental mechanism and AI algorithm: An
example from tuning Poisson's ratio in amorphous networks
|
"AI for science" is widely recognized as a future trend in the development of scientific research. Currently, although machine learning algorithms have played a crucial role in scientific research with numerous successful cases, relatively few instances exist where AI assists researchers in uncovering the underlying physical mechanisms behind a certain phenomenon and subsequently using that mechanism to improve machine learning algorithms' efficiency. This article uses the investigation into the relationship between extreme Poisson's ratio values and the structure of amorphous networks as a case study to illustrate how machine learning methods can assist in revealing underlying physical mechanisms. Upon recognizing that the Poisson's ratio relies on the low-frequency vibrational modes of dynamical matrix, we can then employ a convolutional neural network, trained on the dynamical matrix instead of traditional image recognition, to predict the Poisson's ratio of amorphous networks with a much higher efficiency. Through this example, we aim to showcase the role that artificial intelligence can play in revealing fundamental physical mechanisms, which subsequently improves the machine learning algorithms significantly.
|
http://arxiv.org/pdf/2312.03404v3
|
[
"Changliang Zhu",
"Chenchao Fang",
"Zhipeng Jin",
"Baowen Li",
"Xiangying Shen",
"Lei Xu"
] |
2024-07-09T23:45:34Z
|
2023-12-06T10:40:33Z
|
2407.07275
|
Remastering Divide and Remaster: A Cinematic Audio Source Separation
Dataset with Multilingual Support
|
Cinematic audio source separation (CASS) is a relatively new subtask of audio source separation, concerned with the separation of a mixture into the dialogue, music, and effects stems. To date, only one publicly available dataset exists for CASS, that is, the Divide and Remaster (DnR) dataset, which is currently at version 2. While DnR v2 has been an incredibly useful resource for CASS, several areas of improvement have been identified, particularly through its use in the 2023 Sound Demixing Challenge. In this work, we develop version 3 of the DnR dataset, addressing issues relating to vocal content in non-dialogue stems, loudness distributions, mastering process, and linguistic diversity. In particular, the dialogue stem of DnR v3 includes speech content from more than 30 languages from multiple families including but not limited to the Germanic, Romance, Indo-Aryan, Dravidian, Malayo-Polynesian, and Bantu families. Benchmark results using the Bandit model indicated that training on multilingual data yields significant generalizability to the model even in languages with low data availability. Even in languages with high data availability, the multilingual model often performs on par or better than dedicated models trained on monolingual CASS datasets.
|
http://arxiv.org/pdf/2407.07275v1
|
[
"Karn N. Watcharasupat",
"Chih-Wei Wu",
"Iroro Orife"
] |
2024-07-09T23:39:37Z
|
2024-07-09T23:39:37Z
|
2306.04718
|
Scalable Neural Symbolic Regression using Control Variables
|
Symbolic regression (SR) is a powerful technique for discovering the analytical mathematical expression from data, finding various applications in natural sciences due to its good interpretability of results. However, existing methods face scalability issues when dealing with complex equations involving multiple variables. To address this challenge, we propose ScaleSR, a scalable symbolic regression model that leverages control variables to enhance both accuracy and scalability. The core idea is to decompose multi-variable symbolic regression into a set of single-variable SR problems, which are then combined in a bottom-up manner. The proposed method involves a four-step process. First, we learn a data generator from observed data using deep neural networks (DNNs). Second, the data generator is used to generate samples for a certain variable by controlling the input variables. Thirdly, single-variable symbolic regression is applied to estimate the corresponding mathematical expression. Lastly, we repeat steps 2 and 3 by gradually adding variables one by one until completion. We evaluate the performance of our method on multiple benchmark datasets. Experimental results demonstrate that the proposed ScaleSR significantly outperforms state-of-the-art baselines in discovering mathematical expressions with multiple variables. Moreover, it can substantially reduce the search space for symbolic regression. The source code will be made publicly available upon publication.
|
http://arxiv.org/pdf/2306.04718v2
|
[
"Xieting Chu",
"Hongjue Zhao",
"Enze Xu",
"Hairong Qi",
"Minghan Chen",
"Huajie Shao"
] |
2024-07-09T23:24:53Z
|
2023-06-07T18:30:25Z
|
2403.06903
|
Benign overfitting in leaky ReLU networks with moderate input dimension
|
The problem of benign overfitting asks whether it is possible for a model to perfectly fit noisy training data and still generalize well. We study benign overfitting in two-layer leaky ReLU networks trained with the hinge loss on a binary classification task. We consider input data that can be decomposed into the sum of a common signal and a random noise component, that lie on subspaces orthogonal to one another. We characterize conditions on the signal to noise ratio (SNR) of the model parameters giving rise to benign versus non-benign (or harmful) overfitting: in particular, if the SNR is high then benign overfitting occurs, conversely if the SNR is low then harmful overfitting occurs. We attribute both benign and non-benign overfitting to an approximate margin maximization property and show that leaky ReLU networks trained on hinge loss with gradient descent (GD) satisfy this property. In contrast to prior work we do not require the training data to be nearly orthogonal. Notably, for input dimension $d$ and training sample size $n$, while results in prior work require $d = Omega(n^2 log n)$, here we require only $d = Omegaleft(nright)$.
|
http://arxiv.org/pdf/2403.06903v2
|
[
"Kedar Karhadkar",
"Erin George",
"Michael Murray",
"Guido Montúfar",
"Deanna Needell"
] |
2024-07-09T23:20:12Z
|
2024-03-11T16:56:01Z
|
2407.07258
|
Identification of emotions on Twitter during the 2022 electoral process
in Colombia
|
The study of Twitter as a means for analyzing social phenomena has gained interest in recent years due to the availability of large amounts of data in a relatively spontaneous environment. Within opinion-mining tasks, emotion detection is specially relevant, as it allows for the identification of people's subjective responses to different social events in a more granular way than traditional sentiment analysis based on polarity. In the particular case of political events, the analysis of emotions in social networks can provide valuable information on the perception of candidates, proposals, and other important aspects of the public debate. In spite of this importance, there are few studies on emotion detection in Spanish and, to the best of our knowledge, few resources are public for opinion mining in Colombian Spanish, highlighting the need for generating resources addressing the specific cultural characteristics of this variety. In this work, we present a small corpus of tweets in Spanish related to the 2022 Colombian presidential elections, manually labeled with emotions using a fine-grained taxonomy. We perform classification experiments using supervised state-of-the-art models (BERT models) and compare them with GPT-3.5 in few-shot learning settings. We make our dataset and code publicly available for research purposes.
|
http://arxiv.org/pdf/2407.07258v1
|
[
"Juan Jose Iguaran Fernandez",
"Juan Manuel Perez",
"German Rosati"
] |
2024-07-09T22:26:42Z
|
2024-07-09T22:26:42Z
|
2403.17886
|
Neural Embedding Compression For Efficient Multi-Task Earth Observation
Modelling
|
As repositories of large scale data in earth observation (EO) have grown, so have transfer and storage costs for model training and inference, expending significant resources. We introduce Neural Embedding Compression (NEC), based on the transfer of compressed embeddings to data consumers instead of raw data. We adapt foundation models (FM) through learned neural compression to generate multi-task embeddings while navigating the tradeoff between compression rate and embedding utility. We update only a small fraction of the FM parameters (10%) for a short training period (1% of the iterations of pre-training). We evaluate NEC on two EO tasks: scene classification and semantic segmentation. Compared with applying traditional compression to the raw data, NEC achieves similar accuracy with a 75% to 90% reduction in data. Even at 99.7% compression, performance drops by only 5% on the scene classification task. Overall, NEC is a data-efficient yet performant approach for multi-task EO modelling.
|
http://arxiv.org/pdf/2403.17886v5
|
[
"Carlos Gomes",
"Thomas Brunschwiler"
] |
2024-07-09T22:15:31Z
|
2024-03-26T17:19:23Z
|
2407.07239
|
RotRNN: Modelling Long Sequences with Rotations
|
Linear recurrent models, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling benchmarks. Despite their success, they come with a number of drawbacks, most notably their complex initialisation and normalisation schemes. In this work, we address some of these issues by proposing RotRNN -- a linear recurrent model which utilises the convenient properties of rotation matrices. We show that RotRNN provides a simple model with fewer theoretical assumptions than prior works, with a practical implementation that remains faithful to its theoretical derivation, achieving comparable scores to the LRU and SSMs on several long sequence modelling datasets.
|
http://arxiv.org/pdf/2407.07239v1
|
[
"Rares Dolga",
"Kai Biegun",
"Jake Cunningham",
"David Barber"
] |
2024-07-09T21:37:36Z
|
2024-07-09T21:37:36Z
|
2407.07235
|
Speech After Gender: A Trans-Feminine Perspective on Next Steps for
Speech Science and Technology
|
As experts in voice modification, trans-feminine gender-affirming voice teachers have unique perspectives on voice that confound current understandings of speaker identity. To demonstrate this, we present the Versatile Voice Dataset (VVD), a collection of three speakers modifying their voices along gendered axes. The VVD illustrates that current approaches in speaker modeling, based on categorical notions of gender and a static understanding of vocal texture, fail to account for the flexibility of the vocal tract. Utilizing publicly-available speaker embeddings, we demonstrate that gender classification systems are highly sensitive to voice modification, and speaker verification systems fail to identify voices as coming from the same speaker as voice modification becomes more drastic. As one path towards moving beyond categorical and static notions of speaker identity, we propose modeling individual qualities of vocal texture such as pitch, resonance, and weight.
|
http://arxiv.org/pdf/2407.07235v1
|
[
"Robin Netzorg",
"Alyssa Cote",
"Sumi Koshin",
"Klo Vivienne Garoute",
"Gopala Krishna Anumanchipalli"
] |
2024-07-09T21:19:49Z
|
2024-07-09T21:19:49Z
|
2407.07225
|
ConvNLP: Image-based AI Text Detection
|
The potentials of Generative-AI technologies like Large Language models (LLMs) to revolutionize education are undermined by ethical considerations around their misuse which worsens the problem of academic dishonesty. LLMs like GPT-4 and Llama 2 are becoming increasingly powerful in generating sophisticated content and answering questions, from writing academic essays to solving complex math problems. Students are relying on these LLMs to complete their assignments and thus compromising academic integrity. Solutions to detect LLM-generated text are compute-intensive and often lack generalization. This paper presents a novel approach for detecting LLM-generated AI-text using a visual representation of word embedding. We have formulated a novel Convolutional Neural Network called ZigZag ResNet, as well as a scheduler for improving generalization, named ZigZag Scheduler. Through extensive evaluation using datasets of text generated by six different state-of-the-art LLMs, our model demonstrates strong intra-domain and inter-domain generalization capabilities. Our best model detects AI-generated text with an impressive average detection rate (over inter- and intra-domain test data) of 88.35%. Through an exhaustive ablation study, our ZigZag ResNet and ZigZag Scheduler provide a performance improvement of nearly 4% over the vanilla ResNet. The end-to-end inference latency of our model is below 2.5ms per sentence. Our solution offers a lightweight, computationally efficient, and faster alternative to existing tools for AI-generated text detection, with better generalization performance. It can help academic institutions in their fight against the misuse of LLMs in academic settings. Through this work, we aim to contribute to safeguarding the principles of academic integrity and ensuring the trustworthiness of student work in the era of advanced LLMs.
|
http://arxiv.org/pdf/2407.07225v1
|
[
"Suriya Prakash Jambunathan",
"Ashwath Shankarnarayan",
"Parijat Dube"
] |
2024-07-09T20:44:40Z
|
2024-07-09T20:44:40Z
|
2407.07222
|
SPINEX-Clustering: Similarity-based Predictions with Explainable
Neighbors Exploration for Clustering Problems
|
This paper presents a novel clustering algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) algorithmic family. The newly proposed clustering variant leverages the concept of similarity and higher-order interactions across multiple subspaces to group data into clusters. To showcase the merit of SPINEX, a thorough set of benchmarking experiments was carried out against 13 algorithms, namely, Affinity Propagation, Agglomerative, Birch, DBSCAN, Gaussian Mixture, HDBSCAN, K-Means, KMedoids, Mean Shift, MiniBatch K-Means, OPTICS, Spectral Clustering, and Ward Hierarchical. Then, the performance of all algorithms was examined across 51 synthetic and real datasets from various domains, dimensions, and complexities. Furthermore, we present a companion complexity analysis to compare the complexity of SPINEX to that of the aforementioned algorithms. Our results demonstrate that SPINEX can outperform commonly adopted clustering algorithms by ranking within the top-5 best performing algorithms and has moderate complexity. Finally, a demonstration of the explainability capabilities of SPINEX, along with future research needs, is presented.
|
http://arxiv.org/pdf/2407.07222v1
|
[
"MZ Naser",
"Ahmed Naser"
] |
2024-07-09T20:38:01Z
|
2024-07-09T20:38:01Z
|
2407.07218
|
Weak baselines and reporting biases lead to overoptimism in machine
learning for fluid-related partial differential equations
|
One of the most promising applications of machine learning (ML) in computational physics is to accelerate the solution of partial differential equations (PDEs). The key objective of ML-based PDE solvers is to output a sufficiently accurate solution faster than standard numerical methods, which are used as a baseline comparison. We first perform a systematic review of the ML-for-PDE solving literature. Of articles that use ML to solve a fluid-related PDE and claim to outperform a standard numerical method, we determine that 79% (60/76) compare to a weak baseline. Second, we find evidence that reporting biases, especially outcome reporting bias and publication bias, are widespread. We conclude that ML-for-PDE solving research is overoptimistic: weak baselines lead to overly positive results, while reporting biases lead to underreporting of negative results. To a large extent, these issues appear to be caused by factors similar to those of past reproducibility crises: researcher degrees of freedom and a bias towards positive results. We call for bottom-up cultural changes to minimize biased reporting as well as top-down structural reforms intended to reduce perverse incentives for doing so.
|
http://arxiv.org/pdf/2407.07218v1
|
[
"Nick McGreivy",
"Ammar Hakim"
] |
2024-07-09T20:28:03Z
|
2024-07-09T20:28:03Z
|
2407.08762
|
Commute-Time-Optimised Graphs for GNNs
|
We explore graph rewiring methods that optimise commute time. Recent graph rewiring approaches facilitate long-range interactions in sparse graphs, making such rewirings commute-time-optimal $textit{on average}$. However, when an expert prior exists on which node pairs should or should not interact, a superior rewiring would favour short commute times between these privileged node pairs. We construct two synthetic datasets with known priors reflecting realistic settings, and use these to motivate two bespoke rewiring methods that incorporate the known prior. We investigate the regimes where our rewiring improves test performance on the synthetic datasets. Finally, we perform a case study on a real-world citation graph to investigate the practical implications of our work.
|
http://arxiv.org/pdf/2407.08762v1
|
[
"Igor Sterner",
"Shiye Su",
"Petar Veličković"
] |
2024-07-09T19:31:49Z
|
2024-07-09T19:31:49Z
|
2406.01852
|
Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic
Classification With ECHO
|
With 95% of Internet traffic now encrypted, an effective approach to classifying this traffic is crucial for network security and management. This paper introduces ECHO -- a novel optimization process for ML/DL-based encrypted traffic classification. ECHO targets both classification time and memory utilization and incorporates two innovative techniques. The first component, HO (Hyperparameter Optimization of binnings), aims at creating efficient traffic representations. While previous research often uses representations that map packet sizes and packet arrival times to fixed-sized bins, we show that non-uniform binnings are significantly more efficient. These non-uniform binnings are derived by employing a hyperparameter optimization algorithm in the training stage. HO significantly improves accuracy given a required representation size, or, equivalently, achieves comparable accuracy using smaller representations. Then, we introduce EC (Early Classification of traffic), which enables faster classification using a cascade of classifiers adapted for different exit times, where classification is based on the level of confidence. EC reduces the average classification latency by up to 90%. Remarkably, this method not only maintains classification accuracy but also, in certain cases, improves it. Using three publicly available datasets, we demonstrate that the combined method, Early Classification with Hyperparameter Optimization (ECHO), leads to a significant improvement in classification efficiency.
|
http://arxiv.org/pdf/2406.01852v3
|
[
"Shilo Daum",
"Tal Shapira",
"Anat Bremler-Barr",
"David Hay"
] |
2024-07-09T19:27:34Z
|
2024-06-03T23:54:48Z
|
2405.09312
|
Agnostic Active Learning of Single Index Models with Linear Sample
Complexity
|
We study active learning methods for single index models of the form $F({mathbf x}) = f(langle {mathbf w}, {mathbf x}rangle)$, where $f:mathbb{R} to mathbb{R}$ and ${mathbf x,mathbf w} in mathbb{R}^d$. In addition to their theoretical interest as simple examples of non-linear neural networks, single index models have received significant recent attention due to applications in scientific machine learning like surrogate modeling for partial differential equations (PDEs). Such applications require sample-efficient active learning methods that are robust to adversarial noise. I.e., that work even in the challenging agnostic learning setting. We provide two main results on agnostic active learning of single index models. First, when $f$ is known and Lipschitz, we show that $tilde{O}(d)$ samples collected via {statistical leverage score sampling} are sufficient to learn a near-optimal single index model. Leverage score sampling is simple to implement, efficient, and already widely used for actively learning linear models. Our result requires no assumptions on the data distribution, is optimal up to log factors, and improves quadratically on a recent ${O}(d^{2})$ bound of cite{gajjar2023active}. Second, we show that $tilde{O}(d)$ samples suffice even in the more difficult setting when $f$ is emph{unknown}. Our results leverage tools from high dimensional probability, including Dudley's inequality and dual Sudakov minoration, as well as a novel, distribution-aware discretization of the class of Lipschitz functions.
|
http://arxiv.org/pdf/2405.09312v3
|
[
"Aarshvi Gajjar",
"Wai Ming Tai",
"Xingyu Xu",
"Chinmay Hegde",
"Yi Li",
"Christopher Musco"
] |
2024-07-09T19:20:57Z
|
2024-05-15T13:11:28Z
|
2301.02750
|
Principal Component Analysis in Space Forms
|
Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more appropriate. We study PCA in space forms; that is, those with constant curvatures. At a point on a Riemannian manifold, we can define a Riemannian affine subspace based on a set of tangent vectors. Finding the optimal low-dimensional affine subspace for given points in a space form amounts to dimensionality reduction. Our Space Form PCA (SFPCA) seeks the affine subspace that best represents a set of manifold-valued points with the minimum projection cost. We propose proper cost functions that enjoy two properties: (1) their optimal affine subspace is the solution to an eigenequation, and (2) optimal affine subspaces of different dimensions form a nested set. These properties provide advances over existing methods, which are mostly iterative algorithms with slow convergence and weaker theoretical guarantees. We evaluate the proposed SFPCA on real and simulated data in spherical and hyperbolic spaces. We show that it outperforms alternative methods in estimating true subspaces (in simulated data) with respect to convergence speed or accuracy, often both.
|
http://arxiv.org/pdf/2301.02750v2
|
[
"Puoya Tabaghi",
"Michael Khanzadeh",
"Yusu Wang",
"Sivash Mirarab"
] |
2024-07-09T19:09:28Z
|
2023-01-06T23:48:37Z
|
2311.02213
|
Joint Composite Latent Space Bayesian Optimization
|
Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that employs probabilistic models to identify promising input locations for evaluation. When dealing with composite-structured functions, such as f=g o h, evaluating a specific location x yields observations of both the final outcome f(x) = g(h(x)) as well as the intermediate output(s) h(x). Previous research has shown that integrating information from these intermediate outputs can enhance BO performance substantially. However, existing methods struggle if the outputs h(x) are high-dimensional. Many relevant problems fall into this setting, including in the context of generative AI, molecular design, or robotics. To effectively tackle these challenges, we introduce Joint Composite Latent Space Bayesian Optimization (JoCo), a novel framework that jointly trains neural network encoders and probabilistic models to adaptively compress high-dimensional input and output spaces into manageable latent representations. This enables viable BO on these compressed representations, allowing JoCo to outperform other state-of-the-art methods in high-dimensional BO on a wide variety of simulated and real-world problems.
|
http://arxiv.org/pdf/2311.02213v2
|
[
"Natalie Maus",
"Zhiyuan Jerry Lin",
"Maximilian Balandat",
"Eytan Bakshy"
] |
2024-07-09T19:02:16Z
|
2023-11-03T19:53:37Z
|
2203.07139
|
Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model
Multiplicity
|
While data-driven predictive models are a strictly technological construct, they may operate within a social context in which benign engineering choices entail implicit, indirect and unexpected real-life consequences. Fairness of such systems -- pertaining both to individuals and groups -- is one relevant consideration in this space; algorithms can discriminate people across various protected characteristics regardless of whether these properties are included in the data or discernible through proxy variables. To date, this notion has predominantly been studied for a fixed model, often under different classification thresholds, striving to identify and eradicate undesirable, discriminative and possibly unlawful aspects of its operation. Here, we backtrack on this fixed model assumption to propose and explore a novel definition of cross-model fairness where individuals can be harmed when one predictor is chosen ad hoc from a group of equally well performing models, i.e., in view of utility-based model multiplicity. Since a person may be classified differently across models that are otherwise considered equivalent, this individual could argue for a predictor granting them the most favourable outcome, employing which may have adverse effects on other people. We introduce this scenario with a two-dimensional example and linear classification; then, we present a comprehensive empirical study based on real-life predictive models and data sets that are popular with the algorithmic fairness community; finally, we investigate analytical properties of cross-model fairness and its ramifications in a broader context. Our findings suggest that such unfairness can be readily found in real life and it may be difficult to mitigate by technical means alone as doing so is likely to degrade predictive performance.
|
http://arxiv.org/abs/2203.07139v4
|
[
"Kacper Sokol",
"Meelis Kull",
"Jeffrey Chan",
"Flora Salim"
] |
2024-07-09T19:00:08Z
|
2022-03-14T14:33:39Z
|
2407.07179
|
TrackFormers: In Search of Transformer-Based Particle Tracking for the
High-Luminosity LHC Era
|
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost every step of the data processing pipeline. One such step in need of an overhaul is the task of particle track reconstruction, a.k.a., tracking. A Machine Learning-assisted solution is expected to provide significant improvements, since the most time-consuming step in tracking is the assignment of hits to particles or track candidates. This is the topic of this paper. We take inspiration from large language models. As such, we consider two approaches: the prediction of the next word in a sentence (next hit point in a track), as well as the one-shot prediction of all hits within an event. In an extensive design effort, we have experimented with three models based on the Transformer architecture and one model based on the U-Net architecture, performing track association predictions for collision event hit points. In our evaluation, we consider a spectrum of simple to complex representations of the problem, eliminating designs with lower metrics early on. We report extensive results, covering both prediction accuracy (score) and computational performance. We have made use of the REDVID simulation framework, as well as reductions applied to the TrackML data set, to compose five data sets from simple to complex, for our experiments. The results highlight distinct advantages among different designs in terms of prediction accuracy and computational performance, demonstrating the efficiency of our methodology. Most importantly, the results show the viability of a one-shot encoder-classifier based Transformer solution as a practical approach for the task of tracking.
|
http://arxiv.org/pdf/2407.07179v1
|
[
"Sascha Caron",
"Nadezhda Dobreva",
"Antonio Ferrer Sánchez",
"José D. Martín-Guerrero",
"Uraz Odyurt",
"Roberto Ruiz de Austri Bazan",
"Zef Wolffs",
"Yue Zhao"
] |
2024-07-09T18:47:25Z
|
2024-07-09T18:47:25Z
|
2407.04806
|
Training Guarantees of Neural Network Classification Two-Sample Tests by
Kernel Analysis
|
We construct and analyze a neural network two-sample test to determine whether two datasets came from the same distribution (null hypothesis) or not (alternative hypothesis). We perform time-analysis on a neural tangent kernel (NTK) two-sample test. In particular, we derive the theoretical minimum training time needed to ensure the NTK two-sample test detects a deviation-level between the datasets. Similarly, we derive the theoretical maximum training time before the NTK two-sample test detects a deviation-level. By approximating the neural network dynamics with the NTK dynamics, we extend this time-analysis to the realistic neural network two-sample test generated from time-varying training dynamics and finite training samples. A similar extension is done for the neural network two-sample test generated from time-varying training dynamics but trained on the population. To give statistical guarantees, we show that the statistical power associated with the neural network two-sample test goes to 1 as the neural network training samples and test evaluation samples go to infinity. Additionally, we prove that the training times needed to detect the same deviation-level in the null and alternative hypothesis scenarios are well-separated. Finally, we run some experiments showcasing a two-layer neural network two-sample test on a hard two-sample test problem and plot a heatmap of the statistical power of the two-sample test in relation to training time and network complexity.
|
http://arxiv.org/pdf/2407.04806v2
|
[
"Varun Khurana",
"Xiuyuan Cheng",
"Alexander Cloninger"
] |
2024-07-09T18:45:58Z
|
2024-07-05T18:41:16Z
|
2211.10777
|
Non-Coherent Over-the-Air Decentralized Gradient Descent
|
Implementing Decentralized Gradient Descent (DGD) in wireless systems is challenging due to noise, fading, and limited bandwidth, necessitating topology awareness, transmission scheduling, and the acquisition of channel state information (CSI) to mitigate interference and maintain reliable communications. These operations may result in substantial signaling overhead and scalability challenges in large networks lacking central coordination. This paper introduces a scalable DGD algorithm that eliminates the need for scheduling, topology information, or CSI (both average and instantaneous). At its core is a Non-Coherent Over-The-Air (NCOTA) consensus scheme that exploits a noisy energy superposition property of wireless channels. Nodes encode their local optimization signals into energy levels within an OFDM frame and transmit simultaneously, without coordination. The key insight is that the received energy equals, on average, the sum of the energies of the transmitted signals, scaled by their respective average channel gains, akin to a consensus step. This property enables unbiased consensus estimation, utilizing average channel gains as mixing weights, thereby removing the need for their explicit design or for CSI. Introducing a consensus stepsize mitigates consensus estimation errors due to energy fluctuations around their expected values. For strongly-convex problems, it is shown that the expected squared distance between the local and globally optimum models vanishes at a rate of $mathcal O(1/sqrt{k})$ after $k$ iterations, with suitable decreasing learning and consensus stepsizes. Extensions accommodate a broad class of fading models and frequency-selective channels. Numerical experiments on image classification demonstrate faster convergence in terms of running time compared to state-of-the-art schemes, especially in dense network scenarios.
|
http://arxiv.org/pdf/2211.10777v3
|
[
"Nicolo' Michelusi"
] |
2024-07-09T18:32:09Z
|
2022-11-19T19:15:34Z
|
2406.19300
|
scTree: Discovering Cellular Hierarchies in the Presence of Batch
Effects in scRNA-seq Data
|
We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree corrects for batch effects while simultaneously learning a tree-structured data representation. This VAE-based method allows for a more in-depth understanding of complex cellular landscapes independently of the biasing effects of batches. We show empirically on seven datasets that scTree discovers the underlying clusters of the data and the hierarchical relations between them, as well as outperforms established baseline methods across these datasets. Additionally, we analyze the learned hierarchy to understand its biological relevance, thus underpinning the importance of integrating batch correction directly into the clustering procedure.
|
http://arxiv.org/pdf/2406.19300v2
|
[
"Moritz Vandenhirtz",
"Florian Barkmann",
"Laura Manduchi",
"Julia E. Vogt",
"Valentina Boeva"
] |
2024-07-09T18:17:26Z
|
2024-06-27T16:16:55Z
|
2407.03674
|
Short-Long Policy Evaluation with Novel Actions
|
From incorporating LLMs in education, to identifying new drugs and improving ways to charge batteries, innovators constantly try new strategies in search of better long-term outcomes for students, patients and consumers. One major bottleneck in this innovation cycle is the amount of time it takes to observe the downstream effects of a decision policy that incorporates new interventions. The key question is whether we can quickly evaluate long-term outcomes of a new decision policy without making long-term observations. Organizations often have access to prior data about past decision policies and their outcomes, evaluated over the full horizon of interest. Motivated by this, we introduce a new setting for short-long policy evaluation for sequential decision making tasks. Our proposed methods significantly outperform prior results on simulators of HIV treatment, kidney dialysis and battery charging. We also demonstrate that our methods can be useful for applications in AI safety by quickly identifying when a new decision policy is likely to have substantially lower performance than past policies.
|
http://arxiv.org/pdf/2407.03674v2
|
[
"Hyunji Alex Nam",
"Yash Chandak",
"Emma Brunskill"
] |
2024-07-09T18:05:10Z
|
2024-07-04T06:42:21Z
|
2407.07093
|
FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive
Distillation
|
This work presents a Fully BInarized Large Language Model (FBI-LLM), demonstrating for the first time how to train a large-scale binary language model from scratch (not the partial binary or ternary LLM like BitNet b1.58) to match the performance of its full-precision counterparts (e.g., FP16 or BF16) in transformer-based LLMs. It achieves this by employing an autoregressive distillation (AD) loss with maintaining equivalent model dimensions (130M, 1.3B, 7B) and training data volume as regular LLM pretraining, while delivering competitive results in terms of perplexity and task-specific effectiveness. Intriguingly, by analyzing the training trajectory, we find that the pretrained weight is not necessary for training binarized LLMs from scratch. This research encourages a new computational framework and may facilitate the future design of specialized hardware tailored for fully 1-bit LLMs. We make all models, code, and training dataset fully accessible and transparent to support further research (Code: https://github.com/LiqunMa/FBI-LLM. Model: https://huggingface.co/LiqunMa/).
|
http://arxiv.org/pdf/2407.07093v1
|
[
"Liqun Ma",
"Mingjie Sun",
"Zhiqiang Shen"
] |
2024-07-09T17:59:48Z
|
2024-07-09T17:59:48Z
|
2407.07089
|
Fine-Tuning Linear Layers Only Is a Simple yet Effective Way for Task
Arithmetic
|
Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space, by adding the fine-tuned weights of different tasks. The performance has been further improved by a linear property which is illustrated by weight disentanglement. Yet, conventional linearization methods (e.g., NTK linearization) not only double the time and training cost but also have a disadvantage on single-task performance. We propose a simple yet effective and efficient method that only fine-tunes linear layers, which improves weight disentanglement and efficiency simultaneously. Specifically, our study reveals that only fine-tuning the linear layers in the attention modules makes the whole model occur in a linear regime, significantly improving weight disentanglement. To further understand how our method improves the disentanglement of task arithmetic, we present a comprehensive study of task arithmetic by differentiating the role of representation model and task-specific model. In particular, we find that the representation model plays an important role in improving weight disentanglement whereas the task-specific models such as the classification heads can degenerate the weight disentanglement performance. Overall, our work uncovers novel insights into the fundamental mechanisms of task arithmetic and offers a more reliable and effective approach to editing pre-trained models.
|
http://arxiv.org/pdf/2407.07089v1
|
[
"Ruochen Jin",
"Bojian Hou",
"Jiancong Xiao",
"Weijie Su",
"Li Shen"
] |
2024-07-09T17:59:17Z
|
2024-07-09T17:59:17Z
|
2407.07087
|
CopyBench: Measuring Literal and Non-Literal Reproduction of
Copyright-Protected Text in Language Model Generation
|
Evaluating the degree of reproduction of copyright-protected content by language models (LMs) is of significant interest to the AI and legal communities. Although both literal and non-literal similarities are considered by courts when assessing the degree of reproduction, prior research has focused only on literal similarities. To bridge this gap, we introduce CopyBench, a benchmark designed to measure both literal and non-literal copying in LM generations. Using copyrighted fiction books as text sources, we provide automatic evaluation protocols to assess literal and non-literal copying, balanced against the model utility in terms of the ability to recall facts from the copyrighted works and generate fluent completions. We find that, although literal copying is relatively rare, two types of non-literal copying -- event copying and character copying -- occur even in models as small as 7B parameters. Larger models demonstrate significantly more copying, with literal copying rates increasing from 0.2% to 10.5% and non-literal copying from 2.3% to 6.9% when comparing Llama3-8B and 70B models, respectively. We further evaluate the effectiveness of current strategies for mitigating copying and show that (1) training-time alignment can reduce literal copying but may increase non-literal copying, and (2) current inference-time mitigation methods primarily reduce literal but not non-literal copying.
|
http://arxiv.org/pdf/2407.07087v1
|
[
"Tong Chen",
"Akari Asai",
"Niloofar Mireshghallah",
"Sewon Min",
"James Grimmelmann",
"Yejin Choi",
"Hannaneh Hajishirzi",
"Luke Zettlemoyer",
"Pang Wei Koh"
] |
2024-07-09T17:58:18Z
|
2024-07-09T17:58:18Z
|
2407.07140
|
Cardinality-Aware Set Prediction and Top-$k$ Classification
|
We present a detailed study of cardinality-aware top-$k$ classification, a novel approach that aims to learn an accurate top-$k$ set predictor while maintaining a low cardinality. We introduce a new target loss function tailored to this setting that accounts for both the classification error and the cardinality of the set predicted. To optimize this loss function, we propose two families of surrogate losses: cost-sensitive comp-sum losses and cost-sensitive constrained losses. Minimizing these loss functions leads to new cardinality-aware algorithms that we describe in detail in the case of both top-$k$ and threshold-based classifiers. We establish $H$-consistency bounds for our cardinality-aware surrogate loss functions, thereby providing a strong theoretical foundation for our algorithms. We report the results of extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and SVHN datasets demonstrating the effectiveness and benefits of our cardinality-aware algorithms.
|
http://arxiv.org/pdf/2407.07140v1
|
[
"Corinna Cortes",
"Anqi Mao",
"Christopher Mohri",
"Mehryar Mohri",
"Yutao Zhong"
] |
2024-07-09T17:57:07Z
|
2024-07-09T17:57:07Z
|
2407.07084
|
Stabilized Proximal-Point Methods for Federated Optimization
|
In developing efficient optimization algorithms, it is crucial to account for communication constraints -- a significant challenge in modern federated learning settings. The best-known communication complexity among non-accelerated algorithms is achieved by DANE, a distributed proximal-point algorithm that solves local subproblems in each iteration and that can exploit second-order similarity among individual functions. However, to achieve such communication efficiency, the accuracy requirement for solving the local subproblems is slightly sub-optimal. Inspired by the hybrid projection-proximal point method, in this work, we i) propose a novel distributed algorithm S-DANE. This method adopts a more stabilized prox-center in the proximal step compared with DANE, and matches its deterministic communication complexity. Moreover, the accuracy condition of the subproblem is milder, leading to enhanced local computation efficiency. Furthermore, it supports partial client participation and arbitrary stochastic local solvers, making it more attractive in practice. We further ii) accelerate S-DANE, and show that the resulting algorithm achieves the best-known communication complexity among all existing methods for distributed convex optimization, with the same improved local computation efficiency as S-DANE.
|
http://arxiv.org/pdf/2407.07084v1
|
[
"Xiaowen Jiang",
"Anton Rodomanov",
"Sebastian U. Stich"
] |
2024-07-09T17:56:29Z
|
2024-07-09T17:56:29Z
|
2407.07082
|
Can Learned Optimization Make Reinforcement Learning Less Difficult?
|
While reinforcement learning (RL) holds great potential for decision making in the real world, it suffers from a number of unique difficulties which often need specific consideration. In particular: it is highly non-stationary; suffers from high degrees of plasticity loss; and requires exploration to prevent premature convergence to local optima and maximize return. In this paper, we consider whether learned optimization can help overcome these problems. Our method, Learned Optimization for Plasticity, Exploration and Non-stationarity (OPEN), meta-learns an update rule whose input features and output structure are informed by previously proposed solutions to these difficulties. We show that our parameterization is flexible enough to enable meta-learning in diverse learning contexts, including the ability to use stochasticity for exploration. Our experiments demonstrate that when meta-trained on single and small sets of environments, OPEN outperforms or equals traditionally used optimizers. Furthermore, OPEN shows strong generalization across a distribution of environments and a range of agent architectures.
|
http://arxiv.org/pdf/2407.07082v1
|
[
"Alexander David Goldie",
"Chris Lu",
"Matthew Thomas Jackson",
"Shimon Whiteson",
"Jakob Nicolaus Foerster"
] |
2024-07-09T17:55:23Z
|
2024-07-09T17:55:23Z
|
2407.07071
|
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in
Large Language Models Using Only Attention Maps
|
When asked to summarize articles or answer questions given a passage, large language models (LLMs) can hallucinate details and respond with unsubstantiated answers that are inaccurate with respect to the input context. This paper describes a simple approach for detecting such contextual hallucinations. We hypothesize that contextual hallucinations are related to the extent to which an LLM attends to information in the provided context versus its own generations. Based on this intuition, we propose a simple hallucination detection model whose input features are given by the ratio of attention weights on the context versus newly generated tokens (for each attention head). We find that a linear classifier based on these lookback ratio features is as effective as a richer detector that utilizes the entire hidden states of an LLM or a text-based entailment model. The lookback ratio-based detector -- Lookback Lens -- is found to transfer across tasks and even models, allowing a detector that is trained on a 7B model to be applied (without retraining) to a larger 13B model. We further apply this detector to mitigate contextual hallucinations, and find that a simple classifier-guided decoding approach is able to reduce the amount of hallucination, for example by 9.6% in the XSum summarization task.
|
http://arxiv.org/pdf/2407.07071v1
|
[
"Yung-Sung Chuang",
"Linlu Qiu",
"Cheng-Yu Hsieh",
"Ranjay Krishna",
"Yoon Kim",
"James Glass"
] |
2024-07-09T17:44:34Z
|
2024-07-09T17:44:34Z
|
2306.09397
|
Non-Asymptotic Performance of Social Machine Learning Under Limited Data
|
This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifying a stream of unlabeled data in a distributed manner. In this work, we examine the classification task with limited observations during the decision-making phase, which requires a non-asymptotic performance analysis. We establish a condition for consistent training and derive an upper bound on the probability of error for classification. The results clarify the dependence on the statistical properties of the data and the combination policy used over the graph. They also establish the exponential decay of the probability of error with respect to the number of unlabeled samples.
|
http://arxiv.org/pdf/2306.09397v2
|
[
"Ping Hu",
"Virginia Bordignon",
"Mert Kayaalp",
"Ali H. Sayed"
] |
2024-07-09T17:39:58Z
|
2023-06-15T17:42:14Z
|
2312.00507
|
VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary
Similarity
|
Binary similarity involves determining whether two binary programs exhibit similar functionality, often originating from the same source code. In this work, we propose VexIR2Vec, an approach for binary similarity using VEX-IR, an architecture-neutral Intermediate Representation (IR). We extract the embeddings from sequences of basic blocks, termed peepholes, derived by random walks on the control-flow graph. The peepholes are normalized using transformations inspired by compiler optimizations. The VEX-IR Normalization Engine mitigates, with these transformations, the architectural and compiler-induced variations in binaries while exposing semantic similarities. We then learn the vocabulary of representations at the entity level of the IR using the knowledge graph embedding techniques in an unsupervised manner. This vocabulary is used to derive function embeddings for similarity assessment using VexNet, a feed-forward Siamese network designed to position similar functions closely and separate dissimilar ones in an n-dimensional space. This approach is amenable for both diffing and searching tasks, ensuring robustness against Out-Of-Vocabulary (OOV) issues. We evaluate VexIR2Vec on a dataset comprising 2.7M functions and 15.5K binaries from 7 projects compiled across 12 compilers targeting x86 and ARM architectures. In diffing experiments, VexIR2Vec outperforms the nearest baselines by $40%$, $18%$, $21%$, and $60%$ in cross-optimization, cross-compilation, cross-architecture, and obfuscation settings, respectively. In the searching experiment, VexIR2Vec achieves a mean average precision of $0.76$, outperforming the nearest baseline by $46%$. Our framework is highly scalable and is built as a lightweight, multi-threaded, parallel library using only open-source tools. VexIR2Vec is $3.1$-$3.5 times$ faster than the closest baselines and orders-of-magnitude faster than other tools.
|
http://arxiv.org/pdf/2312.00507v2
|
[
"S. VenkataKeerthy",
"Soumya Banerjee",
"Sayan Dey",
"Yashas Andaluri",
"Raghul PS",
"Subrahmanyam Kalyanasundaram",
"Fernando Magno Quintão Pereira",
"Ramakrishna Upadrasta"
] |
2024-07-09T17:38:42Z
|
2023-12-01T11:22:10Z
|
2407.07064
|
Prompting Techniques for Secure Code Generation: A Systematic
Investigation
|
Large Language Models (LLMs) are gaining momentum in software development with prompt-driven programming enabling developers to create code from natural language (NL) instructions. However, studies have questioned their ability to produce secure code and, thereby, the quality of prompt-generated software. Alongside, various prompting techniques that carefully tailor prompts have emerged to elicit optimal responses from LLMs. Still, the interplay between such prompting strategies and secure code generation remains under-explored and calls for further investigations. OBJECTIVE: In this study, we investigate the impact of different prompting techniques on the security of code generated from NL instructions by LLMs. METHOD: First we perform a systematic literature review to identify the existing prompting techniques that can be used for code generation tasks. A subset of these techniques are evaluated on GPT-3, GPT-3.5, and GPT-4 models for secure code generation. For this, we used an existing dataset consisting of 150 NL security-relevant code-generation prompts. RESULTS: Our work (i) classifies potential prompting techniques for code generation (ii) adapts and evaluates a subset of the identified techniques for secure code generation tasks and (iii) observes a reduction in security weaknesses across the tested LLMs, especially after using an existing technique called Recursive Criticism and Improvement (RCI), contributing valuable insights to the ongoing discourse on LLM-generated code security.
|
http://arxiv.org/pdf/2407.07064v1
|
[
"Catherine Tony",
"Nicolás E. Díaz Ferreyra",
"Markus Mutas",
"Salem Dhiff",
"Riccardo Scandariato"
] |
2024-07-09T17:38:03Z
|
2024-07-09T17:38:03Z
|
2407.07059
|
Differentiable Optimization of Similarity Scores Between Models and
Brains
|
What metrics should guide the development of more realistic models of the brain? One proposal is to quantify the similarity between models and brains using methods such as linear regression, Centered Kernel Alignment (CKA), and angular Procrustes distance. To better understand the limitations of these similarity measures we analyze neural activity recorded in five experiments on nonhuman primates, and optimize synthetic datasets to become more similar to these neural recordings. How similar can these synthetic datasets be to neural activity while failing to encode task relevant variables? We find that some measures like linear regression and CKA, differ from angular Procrustes, and yield high similarity scores even when task relevant variables cannot be linearly decoded from the synthetic datasets. Synthetic datasets optimized to maximize similarity scores initially learn the first principal component of the target dataset, but angular Procrustes captures higher variance dimensions much earlier than methods like linear regression and CKA. We show in both theory and simulations how these scores change when different principal components are perturbed. And finally, we jointly optimize multiple similarity scores to find their allowed ranges, and show that a high angular Procrustes similarity, for example, implies a high CKA score, but not the converse.
|
http://arxiv.org/pdf/2407.07059v1
|
[
"Nathan Cloos",
"Moufan Li",
"Markus Siegel",
"Scott L. Brincat",
"Earl K. Miller",
"Guangyu Robert Yang",
"Christopher J. Cueva"
] |
2024-07-09T17:31:47Z
|
2024-07-09T17:31:47Z
|
2311.11475
|
Gaussian Interpolation Flows
|
Gaussian denoising has emerged as a powerful method for constructing simulation-free continuous normalizing flows for generative modeling. Despite their empirical successes, theoretical properties of these flows and the regularizing effect of Gaussian denoising have remained largely unexplored. In this work, we aim to address this gap by investigating the well-posedness of simulation-free continuous normalizing flows built on Gaussian denoising. Through a unified framework termed Gaussian interpolation flow, we establish the Lipschitz regularity of the flow velocity field, the existence and uniqueness of the flow, and the Lipschitz continuity of the flow map and the time-reversed flow map for several rich classes of target distributions. This analysis also sheds light on the auto-encoding and cycle consistency properties of Gaussian interpolation flows. Additionally, we study the stability of these flows in source distributions and perturbations of the velocity field, using the quadratic Wasserstein distance as a metric. Our findings offer valuable insights into the learning techniques employed in Gaussian interpolation flows for generative modeling, providing a solid theoretical foundation for end-to-end error analyses of learning Gaussian interpolation flows with empirical observations.
|
http://arxiv.org/pdf/2311.11475v2
|
[
"Yuan Gao",
"Jian Huang",
"Yuling Jiao"
] |
2024-07-09T17:30:34Z
|
2023-11-20T00:59:20Z
|
2306.17010
|
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human
Motion Sensing
|
Human motion sensing plays a crucial role in smart systems for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications. To address this, mmWave radars have gained popularity due to their privacy-friendly features. In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable improvements in human activity recognition and human parsing and support human body part tracking.
|
http://arxiv.org/pdf/2306.17010v7
|
[
"Fangqiang Ding",
"Zhen Luo",
"Peijun Zhao",
"Chris Xiaoxuan Lu"
] |
2024-07-09T17:29:35Z
|
2023-06-29T15:06:21Z
|
2407.07055
|
Multicell-Fold: geometric learning in folding multicellular life
|
During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology that defines how living organisms form. Establishing tissue-level morphology critically relies on how every single cell decides to position itself relative to its neighboring cells. Despite its importance, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. To tackle this question, we propose a geometric deep learning model that can predict multicellular folding and embryogenesis, accurately capturing the highly convoluted spatial interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. We successfully use our model to achieve two important tasks, interpretable 4-D morphological sequence alignment, and predicting local cell rearrangements before they occur at single-cell resolution. Furthermore, using an activation map and ablation studies, we demonstrate that cell geometries and cell junction networks together regulate local cell rearrangement which is critical for embryo morphogenesis. This approach provides a novel paradigm to study morphogenesis, highlighting a unified data structure and harnessing the power of geometric deep learning to accurately model the mechanisms and behaviors of cells during development. It offers a pathway toward creating a unified dynamic morphological atlas for a variety of developmental processes such as embryogenesis.
|
http://arxiv.org/pdf/2407.07055v1
|
[
"Haiqian Yang",
"Anh Q. Nguyen",
"Dapeng Bi",
"Markus J. Buehler",
"Ming Guo"
] |
2024-07-09T17:21:49Z
|
2024-07-09T17:21:49Z
|
2407.07054
|
A Differentially Private Blockchain-Based Approach for Vertical
Federated Learning
|
We present the Differentially Private Blockchain-Based Vertical Federal Learning (DP-BBVFL) algorithm that provides verifiability and privacy guarantees for decentralized applications. DP-BBVFL uses a smart contract to aggregate the feature representations, i.e., the embeddings, from clients transparently. We apply local differential privacy to provide privacy for embeddings stored on a blockchain, hence protecting the original data. We provide the first prototype application of differential privacy with blockchain for vertical federated learning. Our experiments with medical data show that DP-BBVFL achieves high accuracy with a tradeoff in training time due to on-chain aggregation. This innovative fusion of differential privacy and blockchain technology in DP-BBVFL could herald a new era of collaborative and trustworthy machine learning applications across several decentralized application domains.
|
http://arxiv.org/pdf/2407.07054v1
|
[
"Linh Tran",
"Sanjay Chari",
"Md. Saikat Islam Khan",
"Aaron Zachariah",
"Stacy Patterson",
"Oshani Seneviratne"
] |
2024-07-09T17:20:49Z
|
2024-07-09T17:20:49Z
|
2405.13136
|
Towards Principled, Practical Policy Gradient for Bandits and Tabular
MDPs
|
We consider (stochastic) softmax policy gradient (PG) methods for bandits and tabular Markov decision processes (MDPs). While the PG objective is non-concave, recent research has used the objective's smoothness and gradient domination properties to achieve convergence to an optimal policy. However, these theoretical results require setting the algorithm parameters according to unknown problem-dependent quantities (e.g. the optimal action or the true reward vector in a bandit problem). To address this issue, we borrow ideas from the optimization literature to design practical, principled PG methods in both the exact and stochastic settings. In the exact setting, we employ an Armijo line-search to set the step-size for softmax PG and demonstrate a linear convergence rate. In the stochastic setting, we utilize exponentially decreasing step-sizes, and characterize the convergence rate of the resulting algorithm. We show that the proposed algorithm offers similar theoretical guarantees as the state-of-the art results, but does not require the knowledge of oracle-like quantities. For the multi-armed bandit setting, our techniques result in a theoretically-principled PG algorithm that does not require explicit exploration, the knowledge of the reward gap, the reward distributions, or the noise. Finally, we empirically compare the proposed methods to PG approaches that require oracle knowledge, and demonstrate competitive performance.
|
http://arxiv.org/pdf/2405.13136v2
|
[
"Michael Lu",
"Matin Aghaei",
"Anant Raj",
"Sharan Vaswani"
] |
2024-07-09T16:59:42Z
|
2024-05-21T18:12:39Z
|
2405.08183
|
Towards Energy-Aware Federated Learning via MARL: A Dual-Selection
Approach for Model and Client
|
Although Federated Learning (FL) is promising in knowledge sharing for heterogeneous Artificial Intelligence of Thing (AIoT) devices, their training performance and energy efficacy are severely restricted in practical battery-driven scenarios due to the ``wooden barrel effect'' caused by the mismatch between homogeneous model paradigms and heterogeneous device capability. As a result, due to various kinds of differences among devices, it is hard for existing FL methods to conduct training effectively in energy-constrained scenarios, such as battery constraints of devices. To tackle the above issues, we propose an energy-aware FL framework named DR-FL, which considers the energy constraints in both clients and heterogeneous deep learning models to enable energy-efficient FL. Unlike Vanilla FL, DR-FL adopts our proposed Muti-Agents Reinforcement Learning (MARL)-based dual-selection method, which allows participated devices to make contributions to the global model effectively and adaptively based on their computing capabilities and energy capacities in a MARL-based manner. Experiments conducted with various widely recognized datasets demonstrate that DR-FL has the capability to optimize the exchange of knowledge among diverse models in large-scale AIoT systems while adhering to energy limitations. Additionally, it improves the performance of each individual heterogeneous device's model.
|
http://arxiv.org/pdf/2405.08183v2
|
[
"Jun Xia",
"Yi Zhang",
"Yiyu Shi"
] |
2024-07-09T16:46:19Z
|
2024-05-13T21:02:31Z
|
2407.07018
|
End-To-End Causal Effect Estimation from Unstructured Natural Language
Data
|
Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions. This increases both the cost and time-to-completion for studies. We show how large, diverse observational text data can be mined with large language models (LLMs) to produce inexpensive causal effect estimates under appropriate causal assumptions. We introduce NATURAL, a novel family of causal effect estimators built with LLMs that operate over datasets of unstructured text. Our estimators use LLM conditional distributions (over variables of interest, given the text data) to assist in the computation of classical estimators of causal effect. We overcome a number of technical challenges to realize this idea, such as automating data curation and using LLMs to impute missing information. We prepare six (two synthetic and four real) observational datasets, paired with corresponding ground truth in the form of randomized trials, which we used to systematically evaluate each step of our pipeline. NATURAL estimators demonstrate remarkable performance, yielding causal effect estimates that fall within 3 percentage points of their ground truth counterparts, including on real-world Phase 3/4 clinical trials. Our results suggest that unstructured text data is a rich source of causal effect information, and NATURAL is a first step towards an automated pipeline to tap this resource.
|
http://arxiv.org/pdf/2407.07018v1
|
[
"Nikita Dhawan",
"Leonardo Cotta",
"Karen Ullrich",
"Rahul G. Krishnan",
"Chris J. Maddison"
] |
2024-07-09T16:38:48Z
|
2024-07-09T16:38:48Z
|
2310.10717
|
A representation learning approach to probe for dynamical dark energy in
matter power spectra
|
We present DE-VAE, a variational autoencoder (VAE) architecture to search for a compressed representation of dynamical dark energy (DE) models in observational studies of the cosmic large-scale structure. DE-VAE is trained on matter power spectra boosts generated at wavenumbers $kin(0.01-2.5) h/rm{Mpc}$ and at four redshift values $zin(0.1,0.48,0.78,1.5)$ for the most typical dynamical DE parametrization with two extra parameters describing an evolving DE equation of state. The boosts are compressed to a lower-dimensional representation, which is concatenated with standard cold dark matter (CDM) parameters and then mapped back to reconstructed boosts; both the compression and the reconstruction components are parametrized as neural networks. Remarkably, we find that a single latent parameter is sufficient to predict 95% (99%) of DE power spectra generated over a broad range of cosmological parameters within $1sigma$ ($2sigma$) of a Gaussian error which includes cosmic variance, shot noise and systematic effects for a Stage IV-like survey. This single parameter shows a high mutual information with the two DE parameters, and these three variables can be linked together with an explicit equation through symbolic regression. Considering a model with two latent variables only marginally improves the accuracy of the predictions, and adding a third latent variable has no significant impact on the model's performance. We discuss how the DE-VAE architecture can be extended from a proof of concept to a general framework to be employed in the search for a common lower-dimensional parametrization of a wide range of beyond-$Lambda$CDM models and for different cosmological datasets. Such a framework could then both inform the development of cosmological surveys by targeting optimal probes, and provide theoretical insight into the common phenomenological aspects of beyond-$Lambda$CDM models.
|
http://arxiv.org/abs/2310.10717v2
|
[
"Davide Piras",
"Lucas Lombriser"
] |
2024-07-09T16:30:26Z
|
2023-10-16T18:00:01Z
|
2312.00128
|
Low latency optical-based mode tracking with machine learning deployed
on FPGAs on a tokamak
|
Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process fast camera data, at rates exceeding 100kfps, on $textit{in situ}$ Field Programmable Gate Array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real-time. Our system utilizes a convolutional neural network (CNN) model which predicts the $n$=1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6$mu$s and a throughput of up to 120kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.
|
http://arxiv.org/abs/2312.00128v3
|
[
"Yumou Wei",
"Ryan F. Forelli",
"Chris Hansen",
"Jeffrey P. Levesque",
"Nhan Tran",
"Joshua C. Agar",
"Giuseppe Di Guglielmo",
"Michael E. Mauel",
"Gerald A. Navratil"
] |
2024-07-09T16:20:06Z
|
2023-11-30T19:00:03Z
|
2407.07004
|
Empirical analysis of Biding Precedent efficiency in the Brazilian
Supreme Court via Similar Case Retrieval
|
Binding precedents (S'umulas Vinculantes) constitute a juridical instrument unique to the Brazilian legal system and whose objectives include the protection of the Federal Supreme Court against repetitive demands. Studies of the effectiveness of these instruments in decreasing the Court's exposure to similar cases, however, indicate that they tend to fail in such a direction, with some of the binding precedents seemingly creating new demands. We empirically assess the legal impact of five binding precedents, 11, 14, 17, 26 and 37, at the highest court level through their effects on the legal subjects they address. This analysis is only possible through the comparison of the Court's ruling about the precedents' themes before they are created, which means that these decisions should be detected through techniques of Similar Case Retrieval. The contributions of this article are therefore twofold: on the mathematical side, we compare the uses of different methods of Natural Language Processing -- TF-IDF, LSTM, BERT, and regex -- for Similar Case Retrieval, whereas on the legal side, we contrast the inefficiency of these binding precedents with a set of hypotheses that may justify their repeated usage. We observe that the deep learning models performed significantly worse in the specific Similar Case Retrieval task and that the reasons for binding precedents to fail in responding to repetitive demand are heterogeneous and case-dependent, making it impossible to single out a specific cause.
|
http://arxiv.org/pdf/2407.07004v1
|
[
"Raphaël Tinarrage",
"Henrique Ennes",
"Lucas E. Resck",
"Lucas T. Gomes",
"Jean R. Ponciano",
"Jorge Poco"
] |
2024-07-09T16:17:16Z
|
2024-07-09T16:17:16Z
|
2407.07000
|
Metron: Holistic Performance Evaluation Framework for LLM Inference
Systems
|
Serving large language models (LLMs) in production can incur substantial costs, which has prompted recent advances in inference system optimizations. Today, these systems are evaluated against conventional latency and throughput metrics (eg. TTFT, TBT, Normalised Latency and TPOT). However, these metrics fail to fully capture the nuances of LLM inference, leading to an incomplete assessment of user-facing performance crucial for real-time applications such as chat and translation. In this paper, we first identify the pitfalls of current performance metrics in evaluating LLM inference systems. We then propose Metron, a comprehensive performance evaluation framework that includes fluidity-index -- a novel metric designed to reflect the intricacies of the LLM inference process and its impact on real-time user experience. Finally, we evaluate various existing open-source platforms and model-as-a-service offerings using Metron, discussing their strengths and weaknesses. Metron is available at https://github.com/project-metron/metron.
|
http://arxiv.org/pdf/2407.07000v1
|
[
"Amey Agrawal",
"Anmol Agarwal",
"Nitin Kedia",
"Jayashree Mohan",
"Souvik Kundu",
"Nipun Kwatra",
"Ramachandran Ramjee",
"Alexey Tumanov"
] |
2024-07-09T16:13:26Z
|
2024-07-09T16:13:26Z
|
2407.06998
|
Changepoint Detection in Highly-Attributed Dynamic Graphs
|
Detecting anomalous behavior in dynamic networks remains a constant challenge. This problem is further exacerbated when the underlying topology of these networks is affected by individual highly-dimensional node attributes. We address this issue by tracking a network's modularity as a proxy of its community structure. We leverage Graph Neural Networks (GNNs) to estimate each snapshot's modularity. GNNs can account for both network structure and high-dimensional node attributes, providing a comprehensive approach for estimating network statistics. Our method is validated through simulations that demonstrate its ability to detect changes in highly-attributed networks by analyzing shifts in modularity. Moreover, we find our method is able to detect a real-world event within the #Iran Twitter reply network, where each node has high-dimensional textual attributes.
|
http://arxiv.org/pdf/2407.06998v1
|
[
"Emiliano Penaloza",
"Nathaniel Stevens"
] |
2024-07-09T16:12:44Z
|
2024-07-09T16:12:44Z
|
2407.04751
|
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off
in Trustworthy Federated Learning
|
In this paper, we first give an introduction to the theoretical basis of the privacy-utility equilibrium in federated learning based on Bayesian privacy definitions and total variation distance privacy definitions. We then present the textit{Learn-to-Distort-Data} framework, which provides a principled approach to navigate the privacy-utility equilibrium by explicitly modeling the distortion introduced by the privacy-preserving mechanism as a learnable variable and optimizing it jointly with the model parameters. We demonstrate the applicability of our framework to a variety of privacy-preserving mechanisms on the basis of data distortion and highlight its connections to related areas such as adversarial training, input robustness, and unlearnable examples. These connections enable leveraging techniques from these areas to design effective algorithms for privacy-utility equilibrium in federated learning under the textit{Learn-to-Distort-Data} framework.
|
http://arxiv.org/pdf/2407.04751v2
|
[
"Xiaojin Zhang",
"Mingcong Xu",
"Wei Chen"
] |
2024-07-09T16:11:04Z
|
2024-07-05T08:15:09Z
|
2407.06992
|
Robust Neural Information Retrieval: An Adversarial and
Out-of-distribution Perspective
|
Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ensuring their reliability in practice, has also garnered significant attention. With a wide array of research on robust IR being proposed, we believe it is the opportune moment to consolidate the current status, glean insights from existing methodologies, and lay the groundwork for future development. We view the robustness of IR to be a multifaceted concept, emphasizing its necessity against adversarial attacks, out-of-distribution (OOD) scenarios and performance variance. With a focus on adversarial and OOD robustness, we dissect robustness solutions for dense retrieval models (DRMs) and neural ranking models (NRMs), respectively, recognizing them as pivotal components of the neural IR pipeline. We provide an in-depth discussion of existing methods, datasets, and evaluation metrics, shedding light on challenges and future directions in the era of large language models. To the best of our knowledge, this is the first comprehensive survey on the robustness of neural IR models, and we will also be giving our first tutorial presentation at SIGIR 2024 url{https://sigir2024-robust-information-retrieval.github.io}. Along with the organization of existing work, we introduce a Benchmark for robust IR (BestIR), a heterogeneous evaluation benchmark for robust neural information retrieval, which is publicly available at url{https://github.com/Davion-Liu/BestIR}. We hope that this study provides useful clues for future research on the robustness of IR models and helps to develop trustworthy search engines url{https://github.com/Davion-Liu/Awesome-Robustness-in-Information-Retrieval}.
|
http://arxiv.org/pdf/2407.06992v1
|
[
"Yu-An Liu",
"Ruqing Zhang",
"Jiafeng Guo",
"Maarten de Rijke",
"Yixing Fan",
"Xueqi Cheng"
] |
2024-07-09T16:07:01Z
|
2024-07-09T16:07:01Z
|
2405.14806
|
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy
Physics
|
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architecture for high-energy physics. L-GATr represents high-energy data in a geometric algebra over four-dimensional space-time and is equivariant under Lorentz transformations, the symmetry group of relativistic kinematics. At the same time, the architecture is a Transformer, which makes it versatile and scalable to large systems. L-GATr is first demonstrated on regression and classification tasks from particle physics. We then construct the first Lorentz-equivariant generative model: a continuous normalizing flow based on an L-GATr network, trained with Riemannian flow matching. Across our experiments, L-GATr is on par with or outperforms strong domain-specific baselines.
|
http://arxiv.org/pdf/2405.14806v2
|
[
"Jonas Spinner",
"Victor Bresó",
"Pim de Haan",
"Tilman Plehn",
"Jesse Thaler",
"Johann Brehmer"
] |
2024-07-09T16:01:23Z
|
2024-05-23T17:15:41Z
|
2407.06979
|
Can virtual staining for high-throughput screening generalize?
|
The large volume and variety of imaging data from high-throughput screening (HTS) in the pharmaceutical industry present an excellent resource for training virtual staining models. However, the potential of models trained under one set of experimental conditions to generalize to other conditions remains underexplored. This study systematically investigates whether data from three cell types (lung, ovarian, and breast) and two phenotypes (toxic and non-toxic conditions) commonly found in HTS can effectively train virtual staining models to generalize across three typical HTS distribution shifts: unseen phenotypes, unseen cell types, and the combination of both. Utilizing a dataset of 772,416 paired bright-field, cytoplasm, nuclei, and DNA-damage stain images, we evaluate the generalization capabilities of models across pixel-based, instance-wise, and biological-feature-based levels. Our findings indicate that training virtual nuclei and cytoplasm models on non-toxic condition samples not only generalizes to toxic condition samples but leads to improved performance across all evaluation levels compared to training on toxic condition samples. Generalization to unseen cell types shows variability depending on the cell type; models trained on ovarian or lung cell samples often perform well under other conditions, while those trained on breast cell samples consistently show poor generalization. Generalization to unseen cell types and phenotypes shows good generalization across all levels of evaluation compared to addressing unseen cell types alone. This study represents the first large-scale, data-centric analysis of the generalization capability of virtual staining models trained on diverse HTS datasets, providing valuable strategies for experimental training data generation.
|
http://arxiv.org/pdf/2407.06979v1
|
[
"Samuel Tonks",
"Cuong Nguyer",
"Steve Hood",
"Ryan Musso",
"Ceridwen Hopely",
"Steve Titus",
"Minh Doan",
"Iain Styles",
"Alexander Krull"
] |
2024-07-09T15:54:06Z
|
2024-07-09T15:54:06Z
|
2405.00914
|
Accelerated Fully First-Order Methods for Bilevel and Minimax
Optimization
|
We present in this paper novel accelerated fully first-order methods in emph{Bilevel Optimization} (BLO). Firstly, for BLO under the assumption that the lower-level functions admit the typical strong convexity assumption, the emph{(Perturbed) Restarted Accelerated Fully First-order methods for Bilevel Approximation} (texttt{PRAF${}^2$BA}) algorithm leveraging emph{fully} first-order oracles is proposed, whereas the algorithm for finding approximate first-order and second-order stationary points with state-of-the-art oracle query complexities in solving complex optimization tasks. Secondly, applying as a special case of BLO the emph{nonconvex-strongly-convex} (NCSC) minimax optimization, texttt{PRAF${}^2$BA} rediscovers emph{perturbed restarted accelerated gradient descent ascent} (texttt{PRAGDA}) that achieves the state-of-the-art complexity for finding approximate second-order stationary points. Additionally, we investigate the challenge of finding stationary points of the hyper-objective function in BLO when lower-level functions lack the typical strong convexity assumption, where we identify several regularity conditions of the lower-level problems that ensure tractability and present hardness results indicating the intractability of BLO for general convex lower-level functions. Under these regularity conditions we propose the emph{Inexact Gradient-Free Method} (texttt{IGFM}), utilizing the emph{Switching Gradient Method} (texttt{SGM}) as an efficient sub-routine to find an approximate stationary point of the hyper-objective in polynomial time. Empirical studies for real-world problems are provided to further validate the outperformance of our proposed algorithms.
|
http://arxiv.org/pdf/2405.00914v3
|
[
"Chris Junchi Li"
] |
2024-07-09T15:48:20Z
|
2024-05-01T23:59:36Z
|
2407.07135
|
Improving Out-of-Distribution Detection by Combining Existing Post-hoc
Methods
|
Since the seminal paper of Hendrycks et al. arXiv:1610.02136, Post-hoc deep Out-of-Distribution (OOD) detection has expanded rapidly. As a result, practitioners working on safety-critical applications and seeking to improve the robustness of a neural network now have a plethora of methods to choose from. However, no method outperforms every other on every dataset arXiv:2210.07242, so the current best practice is to test all the methods on the datasets at hand. This paper shifts focus from developing new methods to effectively combining existing ones to enhance OOD detection. We propose and compare four different strategies for integrating multiple detection scores into a unified OOD detector, based on techniques such as majority vote, empirical and copulas-based Cumulative Distribution Function modeling, and multivariate quantiles based on optimal transport. We extend common OOD evaluation metrics -- like AUROC and FPR at fixed TPR rates -- to these multi-dimensional OOD detectors, allowing us to evaluate them and compare them with individual methods on extensive benchmarks. Furthermore, we propose a series of guidelines to choose what OOD detectors to combine in more realistic settings, i.e. in the absence of known OOD data, relying on principles drawn from Outlier Exposure arXiv:1812.04606. The code is available at https://github.com/paulnovello/multi-ood.
|
http://arxiv.org/pdf/2407.07135v1
|
[
"Paul Novello",
"Yannick Prudent",
"Joseba Dalmau",
"Corentin Friedrich",
"Yann Pequignot"
] |
2024-07-09T15:46:39Z
|
2024-07-09T15:46:39Z
|
2407.04078
|
DotaMath: Decomposition of Thought with Code Assistance and
Self-correction for Mathematical Reasoning
|
Large language models (LLMs) have made impressive progress in handling simple math problems, yet they still struggle with more challenging and complex mathematical tasks. In this paper, we introduce a series of LLMs that employs the Decomposition of thought with code assistance and self-correction for mathematical reasoning, dubbed as DotaMath. DotaMath models tackle complex mathematical tasks by decomposing them into simpler logical subtasks, leveraging code to solve these subtasks, obtaining fine-grained feedback from the code interpreter, and engaging in self-reflection and correction. By annotating diverse interactive tool-use trajectories and employing query evolution on GSM8K and MATH datasets, we generate an instruction fine-tuning dataset called DotaMathQA with 574K query-response pairs. We train a series of base LLMs using imitation learning on DotaMathQA, resulting in DotaMath models that achieve remarkable performance compared to open-source LLMs across various in-domain and out-of-domain benchmarks. Notably, DotaMath-deepseek-7B showcases an outstanding performance of 64.8% on the competitive MATH dataset and 86.7% on GSM8K. Besides, DotaMath-deepseek-7B maintains strong competitiveness on a series of in-domain and out-of-domain benchmarks (Avg. 80.1%). Looking forward, we anticipate that the DotaMath paradigm will open new pathways for addressing intricate mathematical problems. Our code is publicly available at https://github.com/ChengpengLi1003/DotaMath.
|
http://arxiv.org/pdf/2407.04078v2
|
[
"Chengpeng Li",
"Guanting Dong",
"Mingfeng Xue",
"Ru Peng",
"Xiang Wang",
"Dayiheng Liu"
] |
2024-07-09T15:29:03Z
|
2024-07-04T17:39:16Z
|
2407.06946
|
Self-Recognition in Language Models
|
A rapidly growing number of applications rely on a small set of closed-source language models (LMs). This dependency might introduce novel security risks if LMs develop self-recognition capabilities. Inspired by human identity verification methods, we propose a novel approach for assessing self-recognition in LMs using model-generated "security questions". Our test can be externally administered to keep track of frontier models as it does not require access to internal model parameters or output probabilities. We use our test to examine self-recognition in ten of the most capable open- and closed-source LMs currently publicly available. Our extensive experiments found no empirical evidence of general or consistent self-recognition in any examined LM. Instead, our results suggest that given a set of alternatives, LMs seek to pick the "best" answer, regardless of its origin. Moreover, we find indications that preferences about which models produce the best answers are consistent across LMs. We additionally uncover novel insights on position bias considerations for LMs in multiple-choice settings.
|
http://arxiv.org/pdf/2407.06946v1
|
[
"Tim R. Davidson",
"Viacheslav Surkov",
"Veniamin Veselovsky",
"Giuseppe Russo",
"Robert West",
"Caglar Gulcehre"
] |
2024-07-09T15:23:28Z
|
2024-07-09T15:23:28Z
|
2312.02352
|
Working Backwards: Learning to Place by Picking
|
We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which objects must be manipulated to specific, contact-constrained locations. With PvP, we approach the collection of robotic object placement demonstrations by reversing the grasping process and exploiting the inherent symmetry of the pick and place problems. Specifically, we obtain placing demonstrations from a set of grasp sequences of objects initially located at their target placement locations. Our system can collect hundreds of demonstrations in contact-constrained environments without human intervention using two modules: compliant control for grasping and tactile regrasping. We train a policy directly from visual observations through behavioural cloning, using the autonomously-collected demonstrations. By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e.g., placing a plate picked up from a table). We validate our approach in home robot scenarios that include dishwasher loading and table setting. Our approach yields robotic placing policies that outperform policies trained with kinesthetic teaching, both in terms of success rate and data efficiency, while requiring no human supervision.
|
http://arxiv.org/pdf/2312.02352v3
|
[
"Oliver Limoyo",
"Abhisek Konar",
"Trevor Ablett",
"Jonathan Kelly",
"Francois R. Hogan",
"Gregory Dudek"
] |
2024-07-09T15:21:24Z
|
2023-12-04T21:32:00Z
|
2407.06935
|
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and
Theory
|
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertainty quantification. We establish rigorous convergence guarantees of FA-HMC on non-iid distributed data sets, under the strong convexity and Hessian smoothness assumptions. Our analysis investigates the effects of parameter space dimension, noise on gradients and momentum, and the frequency of communication (between the central node and local nodes) on the convergence and communication costs of FA-HMC. Beyond that, we establish the tightness of our analysis by showing that the convergence rate cannot be improved even for continuous FA-HMC process. Moreover, extensive empirical studies demonstrate that FA-HMC outperforms the existing Federated Averaging-Langevin Monte Carlo (FA-LD) algorithm.
|
http://arxiv.org/pdf/2407.06935v1
|
[
"Jiajun Liang",
"Qian Zhang",
"Wei Deng",
"Qifan Song",
"Guang Lin"
] |
2024-07-09T15:10:59Z
|
2024-07-09T15:10:59Z
|
2312.06540
|
Convergence of the Chambolle-Pock Algorithm in the Absence of
Monotonicity
|
The Chambolle-Pock algorithm (CPA), also known as the primal-dual hybrid gradient method, has gained popularity over the last decade due to its success in solving large-scale convex structured problems. This work extends its convergence analysis for problems with varying degrees of (non)monotonicity, quantified through a so-called oblique weak Minty condition on the associated primal-dual operator. Our results reveal novel stepsize and relaxation parameter ranges which do not only depend on the norm of the linear mapping, but also on its other singular values. In particular, in nonmonotone settings, in addition to the classical stepsize conditions, extra bounds on the stepsizes and relaxation parameters are required. On the other hand, in the strongly monotone setting, the relaxation parameter is allowed to exceed the classical upper bound of two. Moreover, we build upon the recently introduced class of semimonotone operators, providing sufficient convergence conditions for CPA when the individual operators are semimonotone. Since this class of operators encompasses traditional operator classes including (hypo)- and co(hypo)-monotone operators, this analysis recovers and extends existing results for CPA. Tightness of the proposed stepsize ranges is demonstrated through several examples.
|
http://arxiv.org/pdf/2312.06540v2
|
[
"Brecht Evens",
"Puya Latafat",
"Panagiotis Patrinos"
] |
2024-07-09T14:51:36Z
|
2023-12-11T17:20:24Z
|
2407.07926
|
Synthetic Data: Revisiting the Privacy-Utility Trade-off
|
Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better trade-off between privacy and utility than traditional anonymization techniques, and that it leads to unpredictable utility loss and highly unpredictable privacy gain. The article also claims to have identified a breach in the differential privacy guarantees provided by PATEGAN and PrivBayes. When a study claims to refute or invalidate prior findings, it is crucial to verify and validate the study. In our work, we analyzed the implementation of the privacy game described in the article and found that it operated in a highly specialized and constrained environment, which limits the applicability of its findings to general cases. Our exploration also revealed that the game did not satisfy a crucial precondition concerning data distributions, which contributed to the perceived violation of the differential privacy guarantees offered by PATEGAN and PrivBayes. We also conducted a privacy-utility trade-off analysis in a more general and unconstrained environment. Our experimentation demonstrated that synthetic data achieves a more favorable privacy-utility trade-off compared to the provided implementation of k-anonymization, thereby reaffirming earlier conclusions.
|
http://arxiv.org/pdf/2407.07926v1
|
[
"Fatima Jahan Sarmin",
"Atiquer Rahman Sarkar",
"Yang Wang",
"Noman Mohammed"
] |
2024-07-09T14:48:43Z
|
2024-07-09T14:48:43Z
|
2407.06910
|
Fine-grained large-scale content recommendations for MSX sellers
|
One of the most critical tasks of Microsoft sellers is to meticulously track and nurture potential business opportunities through proactive engagement and tailored solutions. Recommender systems play a central role to help sellers achieve their goals. In this paper, we present a content recommendation model which surfaces various types of content (technical documentation, comparison with competitor products, customer success stories etc.) that sellers can share with their customers or use for their own self-learning. The model operates at the opportunity level which is the lowest possible granularity and the most relevant one for sellers. It is based on semantic matching between metadata from the contents and carefully selected attributes of the opportunities. Considering the volume of seller-managed opportunities in organizations such as Microsoft, we show how to perform efficient semantic matching over a very large number of opportunity-content combinations. The main challenge is to ensure that the top-5 relevant contents for each opportunity are recommended out of a total of $approx 40,000$ published contents. We achieve this target through an extensive comparison of different model architectures and feature selection. Finally, we further examine the quality of the recommendations in a quantitative manner using a combination of human domain experts as well as by using the recently proposed "LLM as a judge" framework.
|
http://arxiv.org/pdf/2407.06910v1
|
[
"Manpreet Singh",
"Ravdeep Pasricha",
"Ravi Prasad Kondapalli",
"Kiran R",
"Nitish Singh",
"Akshita Agarwalla",
"Manoj R",
"Manish Prabhakar",
"Laurent Boué"
] |
2024-07-09T14:46:09Z
|
2024-07-09T14:46:09Z
|
2407.06909
|
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using
Reinforcement Learning
|
The proliferation of unmanned aerial vehicles (UAVs) in controlled airspace presents significant risks, including potential collisions, disruptions to air traffic, and security threats. Ensuring the safe and efficient operation of airspace, particularly in urban environments and near critical infrastructure, necessitates effective methods to intercept unauthorized or non-cooperative UAVs. This work addresses the critical need for robust, adaptive systems capable of managing such threats through the use of Reinforcement Learning (RL). We present a novel approach utilizing RL to train fixed-wing UAV pursuer agents for intercepting dynamic evader targets. Our methodology explores both model-based and model-free RL algorithms, specifically DreamerV3, Truncated Quantile Critics (TQC), and Soft Actor-Critic (SAC). The training and evaluation of these algorithms were conducted under diverse scenarios, including unseen evasion strategies and environmental perturbations. Our approach leverages high-fidelity flight dynamics simulations to create realistic training environments. This research underscores the importance of developing intelligent, adaptive control systems for UAV interception, significantly contributing to the advancement of secure and efficient airspace management. It demonstrates the potential of RL to train systems capable of autonomously achieving these critical tasks.
|
http://arxiv.org/pdf/2407.06909v1
|
[
"Francisco Giral",
"Ignacio Gómez",
"Soledad Le Clainche"
] |
2024-07-09T14:45:47Z
|
2024-07-09T14:45:47Z
|
2407.01226
|
Bayesian grey-box identification of nonlinear convection effects in heat
transfer dynamics
|
We propose a computational procedure for identifying convection in heat transfer dynamics. The procedure is based on a Gaussian process latent force model, consisting of a white-box component (i.e., known physics) for the conduction and linear convection effects and a Gaussian process that acts as a black-box component for the nonlinear convection effects. States are inferred through Bayesian smoothing and we obtain approximate posterior distributions for the kernel covariance function's hyperparameters using Laplace's method. The nonlinear convection function is recovered from the Gaussian process states using a Bayesian regression model. We validate the procedure by simulation error using the identified nonlinear convection function, on both data from a simulated system and measurements from a physical assembly.
|
http://arxiv.org/pdf/2407.01226v2
|
[
"Wouter M. Kouw",
"Caspar Gruijthuijsen",
"Lennart Blanken",
"Enzo Evers",
"Timothy Rogers"
] |
2024-07-09T14:37:11Z
|
2024-07-01T12:17:01Z
|
2406.15763
|
AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning
|
Existing semi-supervised learning algorithms adopt pseudo-labeling and consistency regulation techniques to introduce supervision signals for unlabeled samples. To overcome the inherent limitation of threshold-based pseudo-labeling, prior studies have attempted to align the confidence threshold with the evolving learning status of the model, which is estimated through the predictions made on the unlabeled data. In this paper, we further reveal that classifier weights can reflect the differentiated learning status across categories and consequently propose a class-specific adaptive threshold mechanism. Additionally, considering that even the optimal threshold scheme cannot resolve the problem of discarding unlabeled samples, a binary classification consistency regulation approach is designed to distinguish candidate classes from negative options for all unlabeled samples. By combining the above strategies, we present a novel SSL algorithm named AllMatch, which achieves improved pseudo-label accuracy and a 100% utilization ratio for the unlabeled data. We extensively evaluate our approach on multiple benchmarks, encompassing both balanced and imbalanced settings. The results demonstrate that AllMatch consistently outperforms existing state-of-the-art methods.
|
http://arxiv.org/pdf/2406.15763v2
|
[
"Zhiyu Wu",
"Jinshi Cui"
] |
2024-07-09T14:35:57Z
|
2024-06-22T06:59:52Z
|
2407.06902
|
Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective
|
One of the primary catalysts fueling advances in artificial intelligence (AI) and machine learning (ML) is the availability of massive, curated datasets. A commonly used technique to curate such massive datasets is crowdsourcing, where data are dispatched to multiple annotators. The annotator-produced labels are then fused to serve downstream learning and inference tasks. This annotation process often creates noisy labels due to various reasons, such as the limited expertise, or unreliability of annotators, among others. Therefore, a core objective in crowdsourcing is to develop methods that effectively mitigate the negative impact of such label noise on learning tasks. This feature article introduces advances in learning from noisy crowdsourced labels. The focus is on key crowdsourcing models and their methodological treatments, from classical statistical models to recent deep learning-based approaches, emphasizing analytical insights and algorithmic developments. In particular, this article reviews the connections between signal processing (SP) theory and methods, such as identifiability of tensor and nonnegative matrix factorization, and novel, principled solutions of longstanding challenges in crowdsourcing -- showing how SP perspectives drive the advancements of this field. Furthermore, this article touches upon emerging topics that are critical for developing cutting-edge AI/ML systems, such as crowdsourcing in reinforcement learning with human feedback (RLHF) and direct preference optimization (DPO) that are key techniques for fine-tuning large language models (LLMs).
|
http://arxiv.org/pdf/2407.06902v1
|
[
"Shahana Ibrahim",
"Panagiotis A. Traganitis",
"Xiao Fu",
"Georgios B. Giannakis"
] |
2024-07-09T14:34:40Z
|
2024-07-09T14:34:40Z
|
2404.09657
|
Sampling for Model Predictive Trajectory Planning in Autonomous Driving
using Normalizing Flows
|
Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches for trajectory generation. In this context, normalizing flows originating from the field of variational inference are considered for the generation of sampling distributions, as they model transformations of simple to more complex distributions. Accordingly, learning-based normalizing flow models are trained for a more efficient exploration of the input domain for the task at hand. The developed algorithm and the proposed sampling distributions are evaluated in two simulation scenarios.
|
http://arxiv.org/pdf/2404.09657v2
|
[
"Georg Rabenstein",
"Lars Ullrich",
"Knut Graichen"
] |
2024-07-09T14:31:07Z
|
2024-04-15T10:45:12Z
|
2405.19346
|
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for
Cross-Subject EEG Motor Imagery Classification
|
Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variability. Previous approaches try to mitigate this using task-specific (TS) EEG signals from the target subject in training. However, recording TS EEG signals requires time and limits its applicability in various fields. In contrast, resting state (RS) EEG signals are a viable alternative due to ease of acquisition with rich subject information. In this paper, we propose a novel subject-adaptive transfer learning strategy that utilizes RS EEG signals to adapt models on unseen subject data. Specifically, we disentangle extracted features into task- and subject-dependent features and use them to calibrate RS EEG signals for obtaining task information while preserving subject characteristics. The calibrated signals are then used to adapt the model to the target subject, enabling the model to simulate processing TS EEG signals of the target subject. The proposed method achieves state-of-the-art accuracy on three public benchmarks, demonstrating the effectiveness of our method in cross-subject EEG MI classification. Our findings highlight the potential of leveraging RS EEG signals to advance practical brain-computer interface systems. The code is available at https://github.com/SionAn/MICCAI2024-ResTL.
|
http://arxiv.org/pdf/2405.19346v2
|
[
"Sion An",
"Myeongkyun Kang",
"Soopil Kim",
"Philip Chikontwe",
"Li Shen",
"Sang Hyun Park"
] |
2024-07-09T14:30:24Z
|
2024-05-17T20:36:04Z
|
2407.06888
|
A Complete Set of Quadratic Constraints For Repeated ReLU
|
This paper derives a complete set of quadratic constraints (QCs) for the repeated ReLU. The complete set of QCs is described by a collection of $2^{n_v}$ matrix copositivity conditions where $n_v$ is the dimension of the repeated ReLU. We also show that only two functions satisfy all QCs in our complete set: the repeated ReLU and a repeated "flipped" ReLU. Thus our complete set of QCs bounds the repeated ReLU as tight as possible up to the sign invariance inherent in quadratic forms. We derive a similar complete set of incremental QCs for repeated ReLU, which can potentially lead to less conservative Lipschitz bounds for ReLU networks than the standard LipSDP approach. Finally, we illustrate the use of the complete set of QCs to assess stability and performance for recurrent neural networks with ReLU activation functions. The stability/performance condition combines Lyapunov/dissipativity theory with the QCs for repeated ReLU. A numerical implementation is given and demonstrated via a simple example.
|
http://arxiv.org/pdf/2407.06888v1
|
[
"Sahel Vahedi Noori",
"Bin Hu",
"Geir Dullerud",
"Peter Seiler"
] |
2024-07-09T14:18:30Z
|
2024-07-09T14:18:30Z
|
2402.10374
|
Revisiting Experience Replayable Conditions
|
Experience replay (ER) used in (deep) reinforcement learning is considered to be applicable only to off-policy algorithms. However, there have been some cases in which ER has been applied for on-policy algorithms, suggesting that off-policyness might be a sufficient condition for applying ER. This paper reconsiders more strict "experience replayable conditions" (ERC) and proposes the way of modifying the existing algorithms to satisfy ERC. In light of this, it is postulated that the instability of policy improvements represents a pivotal factor in ERC. The instability factors are revealed from the viewpoint of metric learning as i) repulsive forces from negative samples and ii) replays of inappropriate experiences. Accordingly, the corresponding stabilization tricks are derived. As a result, it is confirmed through numerical simulations that the proposed stabilization tricks make ER applicable to an advantage actor-critic, an on-policy algorithm. Moreover, its learning performance is comparable to that of a soft actor-critic, a state-of-the-art off-policy algorithm.
|
http://arxiv.org/pdf/2402.10374v2
|
[
"Taisuke Kobayashi"
] |
2024-07-09T14:16:53Z
|
2024-02-15T23:43:53Z
|
2407.06868
|
Energy Efficient Fair STAR-RIS for Mobile Users
|
In this work, we propose a method to improve the energy efficiency and fairness of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) for mobile users, ensuring reduced power consumption while maintaining reliable communication. To achieve this, we introduce a new parameter known as the subsurface assignment variable, which determines the number of STAR-RIS elements allocated to each user. We then formulate a novel optimization problem by concurrently optimizing the phase shifts of the STAR-RIS and subsurface assignment variable. We leverage the deep reinforcement learning (DRL) technique to address this optimization problem. The DRL model predicts the phase shifts of the STAR-RIS and efficiently allocates elements of STAR-RIS to the users. Additionally, we incorporate a penalty term in the DRL model to facilitate intelligent deactivation of STAR-RIS elements when not in use to enhance energy efficiency. Through extensive experiments, we show that the proposed method can achieve fairly high and nearly equal data rates for all users in both the transmission and reflection spaces in an energy-efficient manner.
|
http://arxiv.org/pdf/2407.06868v1
|
[
"Ashok S. Kumar",
"Nancy Nayak",
"Sheetal Kalyani",
"Himal A. Suraweera"
] |
2024-07-09T13:56:59Z
|
2024-07-09T13:56:59Z
|
2405.07800
|
Data Imputation by Pursuing Better Classification: A Supervised
Kernel-Based Method
|
Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows that the pursuit of better classification can guide the data imputation process. While some works consider using label information to assist in this task, their simplistic utilization of labels lacks flexibility and may rely on strict assumptions. In this paper, we propose a new framework that effectively leverages supervision information to complete missing data in a manner conducive to classification. Specifically, this framework operates in two stages. Firstly, it leverages labels to supervise the optimization of similarity relationships among data, represented by the kernel matrix, with the goal of enhancing classification accuracy. To mitigate overfitting that may occur during this process, a perturbation variable is introduced to improve the robustness of the framework. Secondly, the learned kernel matrix serves as additional supervision information to guide data imputation through regression, utilizing the block coordinate descent method. The superiority of the proposed method is evaluated on four real-world data sets by comparing it with state-of-the-art imputation methods. Remarkably, our algorithm significantly outperforms other methods when the data is missing more than 60% of the features
|
http://arxiv.org/pdf/2405.07800v2
|
[
"Ruikai Yang",
"Fan He",
"Mingzhen He",
"Kaijie Wang",
"Xiaolin Huang"
] |
2024-07-09T13:54:24Z
|
2024-05-13T14:44:02Z
|
2209.07341
|
Does CLIP Know My Face?
|
With the rise of deep learning in various applications, privacy concerns around the protection of training data have become a critical area of research. Whereas prior studies have focused on privacy risks in single-modal models, we introduce a novel method to assess privacy for multi-modal models, specifically vision-language models like CLIP. The proposed Identity Inference Attack (IDIA) reveals whether an individual was included in the training data by querying the model with images of the same person. Letting the model choose from a wide variety of possible text labels, the model reveals whether it recognizes the person and, therefore, was used for training. Our large-scale experiments on CLIP demonstrate that individuals used for training can be identified with very high accuracy. We confirm that the model has learned to associate names with depicted individuals, implying the existence of sensitive information that can be extracted by adversaries. Our results highlight the need for stronger privacy protection in large-scale models and suggest that IDIAs can be used to prove the unauthorized use of data for training and to enforce privacy laws.
|
http://arxiv.org/abs/2209.07341v4
|
[
"Dominik Hintersdorf",
"Lukas Struppek",
"Manuel Brack",
"Felix Friedrich",
"Patrick Schramowski",
"Kristian Kersting"
] |
2024-07-09T13:54:11Z
|
2022-09-15T14:48:50Z
|
2407.06862
|
Trust and Resilience in Federated Learning Through Smart Contracts
Enabled Decentralized Systems
|
In this paper, we present a study of a Federated Learning (FL) system, based on the use of decentralized architectures to ensure trust and increase reliability. The system is based on the idea that the FL collaborators upload the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and interact with a dedicated smart contract to track their behavior. Thank to this smart contract, the phases of parameter updates are managed efficiently, thereby strengthening data security. We have carried out an experimental study that exploits two different methods of weight aggregation, i.e., a classic averaging scheme and a federated proximal aggregation. The results confirm the feasibility of the proposal.
|
http://arxiv.org/pdf/2407.06862v1
|
[
"Lorenzo Cassano",
"Jacopo D'Abramo",
"Siraj Munir",
"Stefano Ferretti"
] |
2024-07-09T13:50:32Z
|
2024-07-09T13:50:32Z
|
2407.06855
|
Performance Evaluation of Knowledge Graph Embedding Approaches under
Non-adversarial Attacks
|
Knowledge Graph Embedding (KGE) transforms a discrete Knowledge Graph (KG) into a continuous vector space facilitating its use in various AI-driven applications like Semantic Search, Question Answering, or Recommenders. While KGE approaches are effective in these applications, most existing approaches assume that all information in the given KG is correct. This enables attackers to influence the output of these approaches, e.g., by perturbing the input. Consequently, the robustness of such KGE approaches has to be addressed. Recent work focused on adversarial attacks. However, non-adversarial attacks on all attack surfaces of these approaches have not been thoroughly examined. We close this gap by evaluating the impact of non-adversarial attacks on the performance of 5 state-of-the-art KGE algorithms on 5 datasets with respect to attacks on 3 attack surfaces-graph, parameter, and label perturbation. Our evaluation results suggest that label perturbation has a strong effect on the KGE performance, followed by parameter perturbation with a moderate and graph with a low effect.
|
http://arxiv.org/pdf/2407.06855v1
|
[
"Sourabh Kapoor",
"Arnab Sharma",
"Michael Röder",
"Caglar Demir",
"Axel-Cyrille Ngonga Ngomo"
] |
2024-07-09T13:42:14Z
|
2024-07-09T13:42:14Z
|
2407.06849
|
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly
Detection in Variable-state Multivariate Time-series Data
|
As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. This real-world data is complex in many ways and requires the modelling of testee behaviour. To address this, we propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data. Our approach also avoids the bypass phenomenon and introduces a new method to remap individual windows to a continuous time series. Furthermore, we propose metrics to evaluate the detection delay and root-cause capability of our approach and present results from experiments on a real-world industrial data set. When properly configured, TeVAE flags anomalies only 6% of the time wrongly and detects 65% of anomalies present. It also has the potential to perform well with a smaller training and validation subset but requires a more sophisticated threshold estimation method.
|
http://arxiv.org/pdf/2407.06849v1
|
[
"Lucas Correia",
"Jan-Christoph Goos",
"Philipp Klein",
"Thomas Bäck",
"Anna V. Kononova"
] |
2024-07-09T13:32:33Z
|
2024-07-09T13:32:33Z
|
2405.08556
|
Shape-aware synthesis of pathological lung CT scans using CycleGAN for
enhanced semi-supervised lung segmentation
|
This paper addresses the problem of pathological lung segmentation, a significant challenge in medical image analysis, particularly pronounced in cases of peripheral opacities (severe fibrosis and consolidation) because of the textural similarity between lung tissue and surrounding areas. To overcome these challenges, this paper emphasizes the use of CycleGAN for unpaired image-to-image translation, in order to provide an augmentation method able to generate fake pathological images matching an existing ground truth. Although previous studies have employed CycleGAN, they often neglect the challenge of shape deformation, which is crucial for accurate medical image segmentation. Our work introduces an innovative strategy that incorporates additional loss functions. Specifically, it proposes an L1 loss based on the lung surrounding which shape is constrained to remain unchanged at the transition from the healthy to pathological domains. The lung surrounding is derived based on ground truth lung masks available in the healthy domain. Furthermore, preprocessing steps, such as cropping based on ribs/vertebra locations, are applied to refine the input for the CycleGAN, ensuring that the network focus on the lung region. This is essential to avoid extraneous biases, such as the zoom effect bias, which can divert attention from the main task. The method is applied to enhance in semi-supervised manner the lung segmentation process by employing a U-Net model trained with on-the-fly data augmentation incorporating synthetic pathological tissues generated by the CycleGAN model. Preliminary results from this research demonstrate significant qualitative and quantitative improvements, setting a new benchmark in the field of pathological lung segmentation. Our code is available at https://github.com/noureddinekhiati/Semi-supervised-lung-segmentation
|
http://arxiv.org/pdf/2405.08556v2
|
[
"Rezkellah Noureddine Khiati",
"Pierre-Yves Brillet",
"Aurélien Justet",
"Radu Ispas",
"Catalin Fetita"
] |
2024-07-09T13:32:24Z
|
2024-05-14T12:45:49Z
|
2407.04472
|
EventChat: Implementation and user-centric evaluation of a large
language model-driven conversational recommender system for exploring leisure
events in an SME context
|
Large language models (LLMs) present an enormous evolution in the strategic potential of conversational recommender systems (CRS). Yet to date, research has predominantly focused upon technical frameworks to implement LLM-driven CRS, rather than end-user evaluations or strategic implications for firms, particularly from the perspective of a small to medium enterprises (SME) that makeup the bedrock of the global economy. In the current paper, we detail the design of an LLM-driven CRS in an SME setting, and its subsequent performance in the field using both objective system metrics and subjective user evaluations. While doing so, we additionally outline a short-form revised ResQue model for evaluating LLM-driven CRS, enabling replicability in a rapidly evolving field. Our results reveal good system performance from a user experience perspective (85.5% recommendation accuracy) but underscore latency, cost, and quality issues challenging business viability. Notably, with a median cost of $0.04 per interaction and a latency of 5.7s, cost-effectiveness and response time emerge as crucial areas for achieving a more user-friendly and economically viable LLM-driven CRS for SME settings. One major driver of these costs is the use of an advanced LLM as a ranker within the retrieval-augmented generation (RAG) technique. Our results additionally indicate that relying solely on approaches such as Prompt-based learning with ChatGPT as the underlying LLM makes it challenging to achieve satisfying quality in a production environment. Strategic considerations for SMEs deploying an LLM-driven CRS are outlined, particularly considering trade-offs in the current technical landscape.
|
http://arxiv.org/pdf/2407.04472v3
|
[
"Hannes Kunstmann",
"Joseph Ollier",
"Joel Persson",
"Florian von Wangenheim"
] |
2024-07-09T13:31:00Z
|
2024-07-05T12:42:31Z
|
2403.05171
|
Overcoming Reward Overoptimization via Adversarial Policy Optimization
with Lightweight Uncertainty Estimation
|
We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs). Over-optimization occurs when a reward model serves as an imperfect proxy for human preference, and RL-driven policy optimization erroneously exploits reward inaccuracies. In this paper, we begin by introducing a lightweight way to quantify uncertainties in rewards, relying solely on the last layer embeddings of the reward model, without the need for computationally expensive reward ensembles. AdvPO then addresses a distributionally robust optimization problem centred around the confidence interval of the reward model's predictions for policy improvement. Through comprehensive experiments on the Anthropic HH and TL;DR summarization datasets, we illustrate the efficacy of AdvPO in mitigating the overoptimization issue, consequently resulting in enhanced performance as evaluated through human-assisted evaluation.
|
http://arxiv.org/pdf/2403.05171v2
|
[
"Xiaoying Zhang",
"Jean-Francois Ton",
"Wei Shen",
"Hongning Wang",
"Yang Liu"
] |
2024-07-09T13:17:36Z
|
2024-03-08T09:20:12Z
|
2401.04929
|
Learning-Based Difficulty Calibration for Enhanced Membership Inference
Attacks
|
Machine learning models, in particular deep neural networks, are currently an integral part of various applications, from healthcare to finance. However, using sensitive data to train these models raises concerns about privacy and security. One method that has emerged to verify if the trained models are privacy-preserving is Membership Inference Attacks (MIA), which allows adversaries to determine whether a specific data point was part of a model's training dataset. While a series of MIAs have been proposed in the literature, only a few can achieve high True Positive Rates (TPR) in the low False Positive Rate (FPR) region (0.01%~1%). This is a crucial factor to consider for an MIA to be practically useful in real-world settings. In this paper, we present a novel approach to MIA that is aimed at significantly improving TPR at low FPRs. Our method, named learning-based difficulty calibration for MIA(LDC-MIA), characterizes data records by their hardness levels using a neural network classifier to determine membership. The experiment results show that LDC-MIA can improve TPR at low FPR by up to 4x compared to the other difficulty calibration based MIAs. It also has the highest Area Under ROC curve (AUC) across all datasets. Our method's cost is comparable with most of the existing MIAs, but is orders of magnitude more efficient than one of the state-of-the-art methods, LiRA, while achieving similar performance.
|
http://arxiv.org/pdf/2401.04929v3
|
[
"Haonan Shi",
"Tu Ouyang",
"An Wang"
] |
2024-07-09T12:37:58Z
|
2024-01-10T04:58:17Z
|
2404.04388
|
Mining Potentially Explanatory Patterns via Partial Solutions
|
Genetic Algorithms have established their capability for solving many complex optimization problems. Even as good solutions are produced, the user's understanding of a problem is not necessarily improved, which can lead to a lack of confidence in the results. To mitigate this issue, explainability aims to give insight to the user by presenting them with the knowledge obtained by the algorithm. In this paper we introduce Partial Solutions in order to improve the explainability of solutions to combinatorial optimization problems. Partial Solutions represent beneficial traits found by analyzing a population, and are presented to the user for explainability, but also provide an explicit model from which new solutions can be generated. We present an algorithm that assembles a collection of Partial Solutions chosen to strike a balance between high fitness, simplicity and atomicity. Experiments with standard benchmarks show that the proposed algorithm is able to find Partial Solutions which improve explainability at reasonable computational cost without affecting search performance.
|
http://arxiv.org/pdf/2404.04388v2
|
[
"GianCarlo Catalano",
"Alexander E. I. Brownlee",
"David Cairns",
"John McCall",
"Russell Ainslie"
] |
2024-07-09T12:36:12Z
|
2024-04-05T20:12:02Z
|
2206.02178
|
Factored Conditional Filtering: Tracking States and Estimating
Parameters in High-Dimensional Spaces
|
This paper introduces factored conditional filters, new filtering algorithms for simultaneously tracking states and estimating parameters in high-dimensional state spaces. The conditional nature of the algorithms is used to estimate parameters and the factored nature is used to decompose the state space into low-dimensional subspaces in such a way that filtering on these subspaces gives distributions whose product is a good approximation to the distribution on the entire state space. The conditions for successful application of the algorithms are that observations be available at the subspace level and that the transition model can be factored into local transition models that are approximately confined to the subspaces; these conditions are widely satisfied in computer science, engineering, and geophysical filtering applications. We give experimental results on tracking epidemics and estimating parameters in large contact networks that show the effectiveness of our approach.
|
http://arxiv.org/pdf/2206.02178v2
|
[
"Dawei Chen",
"Samuel Yang-Zhao",
"John Lloyd",
"Kee Siong Ng"
] |
2024-07-09T12:34:28Z
|
2022-06-05T13:37:07Z
|
2407.07133
|
Neuromimetic metaplasticity for adaptive continual learning
|
Conventional intelligent systems based on deep neural network (DNN) models encounter challenges in achieving human-like continual learning due to catastrophic forgetting. Here, we propose a metaplasticity model inspired by human working memory, enabling DNNs to perform catastrophic forgetting-free continual learning without any pre- or post-processing. A key aspect of our approach involves implementing distinct types of synapses from stable to flexible, and randomly intermixing them to train synaptic connections with different degrees of flexibility. This strategy allowed the network to successfully learn a continuous stream of information, even under unexpected changes in input length. The model achieved a balanced tradeoff between memory capacity and performance without requiring additional training or structural modifications, dynamically allocating memory resources to retain both old and new information. Furthermore, the model demonstrated robustness against data poisoning attacks by selectively filtering out erroneous memories, leveraging the Hebb repetition effect to reinforce the retention of significant data.
|
http://arxiv.org/pdf/2407.07133v1
|
[
"Suhee Cho",
"Hyeonsu Lee",
"Seungdae Baek",
"Se-Bum Paik"
] |
2024-07-09T12:21:35Z
|
2024-07-09T12:21:35Z
|
2407.06800
|
Learn and Don't Forget: Adding a New Language to ASR Foundation Models
|
Foundation ASR models often support many languages, e.g. 100 languages in Whisper. However, there has been limited work on integrating an additional, typically low-resource, language, while maintaining performance on the original language set. Fine-tuning, while simple, may degrade the accuracy of the original set. We compare three approaches that exploit adaptation parameters: soft language code tuning, train only the language code; soft prompt tuning, train prepended tokens; and LoRA where a small set of additional parameters are optimised. Elastic Weight Consolidation (EWC) offers an alternative compromise with the potential to maintain performance in specific target languages. Results show that direct fine-tuning yields the best performance for the new language but degrades existing language capabilities. EWC can address this issue for specific languages. If only adaptation parameters are used, the language capabilities are maintained but at the cost of performance in the new language.
|
http://arxiv.org/pdf/2407.06800v1
|
[
"Mengjie Qian",
"Siyuan Tang",
"Rao Ma",
"Kate M. Knill",
"Mark J. F. Gales"
] |
2024-07-09T12:14:48Z
|
2024-07-09T12:14:48Z
|
2404.11100
|
Synthesizing Realistic Data for Table Recognition
|
To overcome the limitations and challenges of current automatic table data annotation methods and random table data synthesis approaches, we propose a novel method for synthesizing annotation data specifically designed for table recognition. This method utilizes the structure and content of existing complex tables, facilitating the efficient creation of tables that closely replicate the authentic styles found in the target domain. By leveraging the actual structure and content of tables from Chinese financial announcements, we have developed the first extensive table annotation dataset in this domain. We used this dataset to train several recent deep learning-based end-to-end table recognition models. Additionally, we have established the inaugural benchmark for real-world complex tables in the Chinese financial announcement domain, using it to assess the performance of models trained on our synthetic data, thereby effectively validating our method's practicality and effectiveness. Furthermore, we applied our synthesis method to augment the FinTabNet dataset, extracted from English financial announcements, by increasing the proportion of tables with multiple spanning cells to introduce greater complexity. Our experiments show that models trained on this augmented dataset achieve comprehensive improvements in performance, especially in the recognition of tables with multiple spanning cells.
|
http://arxiv.org/pdf/2404.11100v2
|
[
"Qiyu Hou",
"Jun Wang",
"Meixuan Qiao",
"Lujun Tian"
] |
2024-07-09T12:09:32Z
|
2024-04-17T06:36:17Z
|
2407.06797
|
ED-VAE: Entropy Decomposition of ELBO in Variational Autoencoders
|
Traditional Variational Autoencoders (VAEs) are constrained by the limitations of the Evidence Lower Bound (ELBO) formulation, particularly when utilizing simplistic, non-analytic, or unknown prior distributions. These limitations inhibit the VAE's ability to generate high-quality samples and provide clear, interpretable latent representations. This work introduces the Entropy Decomposed Variational Autoencoder (ED-VAE), a novel re-formulation of the ELBO that explicitly includes entropy and cross-entropy components. This reformulation significantly enhances model flexibility, allowing for the integration of complex and non-standard priors. By providing more detailed control over the encoding and regularization of latent spaces, ED-VAE not only improves interpretability but also effectively captures the complex interactions between latent variables and observed data, thus leading to better generative performance.
|
http://arxiv.org/pdf/2407.06797v1
|
[
"Fotios Lygerakis",
"Elmar Rueckert"
] |
2024-07-09T12:09:21Z
|
2024-07-09T12:09:21Z
|
2104.12333
|
Explore BiLSTM-CRF-Based Models for Open Relation Extraction
|
Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks. In this paper, we develop several Open RE models based on the bidirectional LSTM-CRF (BiLSTM-CRF) neural network and different contextualized word embedding methods. We also propose a new tagging scheme to solve overlapping problems and enhance models' performance. From the evaluation results and comparisons between models, we select the best combination of tagging scheme, word embedder, and BiLSTM-CRF network to achieve an Open RE model with a remarkable extracting ability on multiple-relation sentences.
|
http://arxiv.org/pdf/2104.12333v2
|
[
"Tao Ni",
"Qing Wang",
"Gabriela Ferraro"
] |
2024-07-09T12:06:39Z
|
2021-04-26T03:37:22Z
|
2406.15568
|
Robust Reinforcement Learning from Corrupted Human Feedback
|
Reinforcement learning from human feedback (RLHF) provides a principled framework for aligning AI systems with human preference data. For various reasons, e.g., personal bias, context ambiguity, lack of training, etc, human annotators may give incorrect or inconsistent preference labels. To tackle this challenge, we propose a robust RLHF approach -- $R^3M$, which models the potentially corrupted preference label as sparse outliers. Accordingly, we formulate the robust reward learning as an $ell_1$-regularized maximum likelihood estimation problem. Computationally, we develop an efficient alternating optimization algorithm, which only incurs negligible computational overhead compared with the standard RLHF approach. Theoretically, we prove that under proper regularity conditions, $R^3M$ can consistently learn the underlying reward and identify outliers, provided that the number of outlier labels scales sublinearly with the preference sample size. Furthermore, we remark that $R^3M$ is versatile and can be extended to various preference optimization methods, including direct preference optimization (DPO). Our experiments on robotic control and natural language generation with large language models (LLMs) show that $R^3M$ improves robustness of the reward against several types of perturbations to the preference data.
|
http://arxiv.org/pdf/2406.15568v2
|
[
"Alexander Bukharin",
"Ilgee Hong",
"Haoming Jiang",
"Zichong Li",
"Qingru Zhang",
"Zixuan Zhang",
"Tuo Zhao"
] |
2024-07-09T12:04:03Z
|
2024-06-21T18:06:30Z
|
2305.18256
|
Representation Learning on Hyper-Relational and Numeric Knowledge Graphs
with Transformers
|
A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete objects, some information should be represented using numeric values, e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford Univ.) can be associated with a qualifier such as (start time, 1911). In this paper, we propose a unified framework named HyNT that learns representations of a hyper-relational knowledge graph containing numeric literals in either triplets or qualifiers. We define a context transformer and a prediction transformer to learn the representations based not only on the correlations between a triplet and its qualifiers but also on the numeric information. By learning compact representations of triplets and qualifiers and feeding them into the transformers, we reduce the computation cost of using transformers. Using HyNT, we can predict missing numeric values in addition to missing entities or relations in a hyper-relational knowledge graph. Experimental results show that HyNT significantly outperforms state-of-the-art methods on real-world datasets.
|
http://arxiv.org/abs/2305.18256v4
|
[
"Chanyoung Chung",
"Jaejun Lee",
"Joyce Jiyoung Whang"
] |
2024-07-09T12:03:09Z
|
2023-05-29T17:23:31Z
|
2403.02181
|
Not All Layers of LLMs Are Necessary During Inference
|
Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. However, not all requests posed to LLMs are equally difficult to handle. Through analysis, we show that for some tasks, LLMs can achieve results comparable to the final output at some intermediate layers. That is, not all layers of LLMs are necessary during inference. If we can predict at which layer the inferred results match the final results (produced by evaluating all layers), we could significantly reduce the inference cost. To this end, we propose a simple yet effective algorithm named AdaInfer to adaptively terminate the inference process for an input instance. AdaInfer relies on easily obtainable statistical features and classic classifiers like SVM. Experiments on well-known LLMs like the Llama2 series and OPT, show that AdaInfer can achieve an average of 17.8% pruning ratio, and up to 43% on sentiment tasks, with nearly no performance drop (<1%). Because AdaInfer does not alter LLM parameters, the LLMs incorporated with AdaInfer maintain generalizability across tasks.
|
http://arxiv.org/pdf/2403.02181v3
|
[
"Siqi Fan",
"Xin Jiang",
"Xiang Li",
"Xuying Meng",
"Peng Han",
"Shuo Shang",
"Aixin Sun",
"Yequan Wang",
"Zhongyuan Wang"
] |
2024-07-09T11:59:01Z
|
2024-03-04T16:23:58Z
|
2407.06785
|
Towards physics-informed neural networks for landslide prediction
|
For decades, solutions to regional scale landslide prediction have mostly relied on data-driven models, by definition, disconnected from the physics of the failure mechanism. The success and spread of such tools came from the ability to exploit proxy variables rather than explicit geotechnical ones, as the latter are prohibitive to acquire over broad landscapes. Our work implements a Physics Informed Neural Network (PINN) approach, thereby adding to a standard data-driven architecture, an intermediate constraint to solve for the permanent deformation typical of Newmark slope stability methods. This translates into a neural network tasked with explicitly retrieving geotechnical parameters from common proxy variables and then minimize a loss function with respect to the available coseismic landside inventory. The results are very promising, because our model not only produces excellent predictive performance in the form of standard susceptibility output, but in the process, also generates maps of the expected geotechnical properties at a regional scale. Such architecture is therefore framed to tackle coseismic landslide prediction, something that, if confirmed in other studies, could open up towards PINN-based near-real-time predictions.
|
http://arxiv.org/pdf/2407.06785v1
|
[
"Ashok Dahal",
"Luigi Lombardo"
] |
2024-07-09T11:54:49Z
|
2024-07-09T11:54:49Z
|
2407.06783
|
Convergence rates for Poisson learning to a Poisson equation with
measure data
|
In this paper we prove discrete to continuum convergence rates for Poisson Learning, a graph-based semi-supervised learning algorithm that is based on solving the graph Poisson equation with a source term consisting of a linear combination of Dirac deltas located at labeled points and carrying label information. The corresponding continuum equation is a Poisson equation with measure data in a Euclidean domain $Omega subset mathbb{R}^d$. The singular nature of these equations is challenging and requires an approach with several distinct parts: (1) We prove quantitative error estimates when convolving the measure data of a Poisson equation with (approximately) radial function supported on balls. (2) We use quantitative variational techniques to prove discrete to continuum convergence rates on random geometric graphs with bandwidth $varepsilon>0$ for bounded source terms. (3) We show how to regularize the graph Poisson equation via mollification with the graph heat kernel, and we study fine asymptotics of the heat kernel on random geometric graphs. Combining these three pillars we obtain $L^1$ convergence rates that scale, up to logarithmic factors, like $O(varepsilon^{frac{1}{d+2}})$ for general data distributions, and $O(varepsilon^{frac{2-sigma}{d+4}})$ for uniformly distributed data, where $sigma>0$. These rates are valid with high probability if $varepsilonggleft({log n}/{n}right)^q$ where $n$ denotes the number of vertices of the graph and $q approx frac{1}{3d}$.
|
http://arxiv.org/pdf/2407.06783v1
|
[
"Leon Bungert",
"Jeff Calder",
"Max Mihailescu",
"Kodjo Houssou",
"Amber Yuan"
] |
2024-07-09T11:54:34Z
|
2024-07-09T11:54:34Z
|
2407.06771
|
Temporal Convolution Derived Multi-Layered Reservoir Computing
|
The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological processes. Especially chaotic time series that depend on a long history pose an exceptionally difficult problem. While machine learning has shown to be a promising approach for predicting such time series, it either demands long training time and much training data when using deep recurrent neural networks. Alternative, when using a reservoir computing approach it comes with high uncertainty and typically a high number of random initializations and extensive hyper-parameter tuning when using a reservoir computing approach. In this paper, we focus on the reservoir computing approach and propose a new mapping of input data into the reservoir's state space. Furthermore, we incorporate this method in two novel network architectures increasing parallelizability, depth and predictive capabilities of the neural network while reducing the dependence on randomness. For the evaluation, we approximate a set of time series from the Mackey-Glass equation, inhabiting non-chaotic as well as chaotic behavior and compare our approaches in regard to their predictive capabilities to echo state networks and gated recurrent units. For the chaotic time series, we observe an error reduction of up to $85.45%$ and up to $87.90%$ in contrast to echo state networks and gated recurrent units respectively. Furthermore, we also observe tremendous improvements for non-chaotic time series of up to $99.99%$ in contrast to existing approaches.
|
http://arxiv.org/pdf/2407.06771v1
|
[
"Johannes Viehweg",
"Dominik Walther",
"Prof. Dr. -Ing. Patrick Mäder"
] |
2024-07-09T11:40:46Z
|
2024-07-09T11:40:46Z
|
2407.06765
|
A Generalization Bound for Nearly-Linear Networks
|
We consider nonlinear networks as perturbations of linear ones. Based on this approach, we present novel generalization bounds that become non-vacuous for networks that are close to being linear. The main advantage over the previous works which propose non-vacuous generalization bounds is that our bounds are a-priori: performing the actual training is not required for evaluating the bounds. To the best of our knowledge, they are the first non-vacuous generalization bounds for neural nets possessing this property.
|
http://arxiv.org/pdf/2407.06765v1
|
[
"Eugene Golikov"
] |
2024-07-09T11:20:01Z
|
2024-07-09T11:20:01Z
|
2407.06756
|
Frequency and Generalisation of Periodic Activation Functions in
Reinforcement Learning
|
Periodic activation functions, often referred to as learned Fourier features have been widely demonstrated to improve sample efficiency and stability in a variety of deep RL algorithms. Potentially incompatible hypotheses have been made about the source of these improvements. One is that periodic activations learn low frequency representations and as a result avoid overfitting to bootstrapped targets. Another is that periodic activations learn high frequency representations that are more expressive, allowing networks to quickly fit complex value functions. We analyse these claims empirically, finding that periodic representations consistently converge to high frequencies regardless of their initialisation frequency. We also find that while periodic activation functions improve sample efficiency, they exhibit worse generalization on states with added observation noise -- especially when compared to otherwise equivalent networks with ReLU activation functions. Finally, we show that weight decay regularization is able to partially offset the overfitting of periodic activation functions, delivering value functions that learn quickly while also generalizing.
|
http://arxiv.org/pdf/2407.06756v1
|
[
"Augustine N. Mavor-Parker",
"Matthew J. Sargent",
"Caswell Barry",
"Lewis Griffin",
"Clare Lyle"
] |
2024-07-09T11:07:41Z
|
2024-07-09T11:07:41Z
|
2407.07128
|
Modularity aided consistent attributed graph clustering via coarsening
|
Graph clustering is an important unsupervised learning technique for partitioning graphs with attributes and detecting communities. However, current methods struggle to accurately capture true community structures and intra-cluster relations, be computationally efficient, and identify smaller communities. We address these challenges by integrating coarsening and modularity maximization, effectively leveraging both adjacency and node features to enhance clustering accuracy. We propose a loss function incorporating log-determinant, smoothness, and modularity components using a block majorization-minimization technique, resulting in superior clustering outcomes. The method is theoretically consistent under the Degree-Corrected Stochastic Block Model (DC-SBM), ensuring asymptotic error-free performance and complete label recovery. Our provably convergent and time-efficient algorithm seamlessly integrates with graph neural networks (GNNs) and variational graph autoencoders (VGAEs) to learn enhanced node features and deliver exceptional clustering performance. Extensive experiments on benchmark datasets demonstrate its superiority over existing state-of-the-art methods for both attributed and non-attributed graphs.
|
http://arxiv.org/pdf/2407.07128v1
|
[
"Samarth Bhatia",
"Yukti Makhija",
"Manoj Kumar",
"Sandeep Kumar"
] |
2024-07-09T10:42:19Z
|
2024-07-09T10:42:19Z
|
2209.07863
|
Expanding continual few-shot learning benchmarks to include recognition
of specific instances
|
Continual learning and few-shot learning are important frontiers in progress toward broader Machine Learning (ML) capabilities. Recently, there has been intense interest in combining both. One of the first examples to do so was the Continual few-shot Learning (CFSL) framework of Antoniou et al. arXiv:2004.11967. In this study, we extend CFSL in two ways that capture a broader range of challenges, important for intelligent agent behaviour in real-world conditions. First, we increased the number of classes by an order of magnitude, making the results more comparable to standard continual learning experiments. Second, we introduced an 'instance test' which requires recognition of specific instances of classes -- a capability of animal cognition that is usually neglected in ML. For an initial exploration of ML model performance under these conditions, we selected representative baseline models from the original CFSL work and added a model variant with replay. As expected, learning more classes is more difficult than the original CFSL experiments, and interestingly, the way in which image instances and classes are presented affects classification performance. Surprisingly, accuracy in the baseline instance test is comparable to other classification tasks, but poor given significant occlusion and noise. The use of replay for consolidation substantially improves performance for both types of tasks, but particularly for the instance test.
|
http://arxiv.org/abs/2209.07863v4
|
[
"Gideon Kowadlo",
"Abdelrahman Ahmed",
"Amir Mayan",
"David Rawlinson"
] |
2024-07-09T10:41:17Z
|
2022-08-26T06:32:55Z
|
2407.06740
|
Positive-Unlabelled Learning for Improving Image-based Recommender
System Explainability
|
Among the existing approaches for visual-based Recommender System (RS) explainability, utilizing user-uploaded item images as efficient, trustable explanations is a promising option. However, current models following this paradigm assume that, for any user, all images uploaded by other users can be considered negative training examples (i.e. bad explanatory images), an inadvertedly naive labelling assumption that contradicts the rationale of the approach. This work proposes a new explainer training pipeline by leveraging Positive-Unlabelled (PU) Learning techniques to train image-based explainer with refined subsets of reliable negative examples for each user selected through a novel user-personalized, two-step, similarity-based PU Learning algorithm. Computational experiments show this PU-based approach outperforms the state-of-the-art non-PU method in six popular real-world datasets, proving that an improvement of visual-based RS explainability can be achieved by maximizing training data quality rather than increasing model complexity.
|
http://arxiv.org/pdf/2407.06740v1
|
[
"Álvaro Fernández-Campa-González",
"Jorge Paz-Ruza",
"Amparo Alonso-Betanzos",
"Bertha Guijarro-Berdiñas"
] |
2024-07-09T10:40:31Z
|
2024-07-09T10:40:31Z
|
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