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2407.02607
|
Product Geometries on Cholesky Manifolds with Applications to SPD
Manifolds
|
This paper presents two new metrics on the Symmetric Positive Definite (SPD) manifold via the Cholesky manifold, i.e., the space of lower triangular matrices with positive diagonal elements. We first unveil that the existing popular Riemannian metric on the Cholesky manifold can be generally characterized as the product metric of a Euclidean metric and a Riemannian metric on the space of n-dimensional positive vectors. Based on this analysis, we propose two novel metrics on the Cholesky manifolds, i.e., Diagonal Power Euclidean Metric and Diagonal Generalized Bures-Wasserstein Metric, which are numerically stabler than the existing Cholesky metric. We also discuss the gyro structures and deformed metrics associated with our metrics. The gyro structures connect the linear and geometric properties, while the deformed metrics interpolate between our proposed metrics and the existing metric. Further, by Cholesky decomposition, the proposed deformed metrics and gyro structures are pulled back to SPD manifolds. Compared with existing Riemannian metrics on SPD manifolds, our metrics are easy to use, computationally efficient, and numerically stable.
|
http://arxiv.org/pdf/2407.02607v1
|
[
"Ziheng Chen",
"Yue Song",
"Xiao-Jun Wu",
"Nicu Sebe"
] |
2024-07-02T18:46:13Z
|
2024-07-02T18:46:13Z
|
2407.02604
|
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data
and eXpert model predictions
|
Large vision language models (VLMs) have progressed incredibly from research to applicability for general-purpose use cases. LLaVA-Med, a pioneering large language and vision assistant for biomedicine, can perform multi-modal biomedical image and data analysis to provide a natural language interface for radiologists. While it is highly generalizable and works with multi-modal data, it is currently limited by well-known challenges that exist in the large language model space. Hallucinations and imprecision in responses can lead to misdiagnosis which currently hinder the clinical adaptability of VLMs. To create precise, user-friendly models in healthcare, we propose D-Rax -- a domain-specific, conversational, radiologic assistance tool that can be used to gain insights about a particular radiologic image. In this study, we enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting, offering comprehensive insights from medical imaging and aiding in the formulation of accurate diagnosis. D-Rax is achieved by fine-tuning the LLaVA-Med architecture on our curated enhanced instruction-following data, comprising of images, instructions, as well as disease diagnosis and demographic predictions derived from MIMIC-CXR imaging data, CXR-related visual question answer (VQA) pairs, and predictive outcomes from multiple expert AI models. We observe statistically significant improvement in responses when evaluated for both open and close-ended conversations. Leveraging the power of state-of-the-art diagnostic models combined with VLMs, D-Rax empowers clinicians to interact with medical images using natural language, which could potentially streamline their decision-making process, enhance diagnostic accuracy, and conserve their time.
|
http://arxiv.org/pdf/2407.02604v1
|
[
"Hareem Nisar",
"Syed Muhammad Anwar",
"Zhifan Jiang",
"Abhijeet Parida",
"Vishwesh Nath",
"Holger R. Roth",
"Marius George Linguraru"
] |
2024-07-02T18:43:10Z
|
2024-07-02T18:43:10Z
|
2407.02601
|
Linear Submodular Maximization with Bandit Feedback
|
Submodular optimization with bandit feedback has recently been studied in a variety of contexts. In a number of real-world applications such as diversified recommender systems and data summarization, the submodular function exhibits additional linear structure. We consider developing approximation algorithms for the maximization of a submodular objective function $f:2^Utomathbb{R}_{geq 0}$, where $f=sum_{i=1}^dw_iF_{i}$. It is assumed that we have value oracle access to the functions $F_i$, but the coefficients $w_i$ are unknown, and $f$ can only be accessed via noisy queries. We develop algorithms for this setting inspired by adaptive allocation algorithms in the best-arm identification for linear bandit, with approximation guarantees arbitrarily close to the setting where we have value oracle access to $f$. Finally, we empirically demonstrate that our algorithms make vast improvements in terms of sample efficiency compared to algorithms that do not exploit the linear structure of $f$ on instances of move recommendation.
|
http://arxiv.org/pdf/2407.02601v1
|
[
"Wenjing Chen",
"Victoria G. Crawford"
] |
2024-07-02T18:40:52Z
|
2024-07-02T18:40:52Z
|
2407.02599
|
Meta 3D Gen
|
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports physically-based rendering (PBR), necessary for 3D asset relighting in real-world applications. Additionally, 3DGen supports generative retexturing of previously generated (or artist-created) 3D shapes using additional textual inputs provided by the user. 3DGen integrates key technical components, Meta 3D AssetGen and Meta 3D TextureGen, that we developed for text-to-3D and text-to-texture generation, respectively. By combining their strengths, 3DGen represents 3D objects simultaneously in three ways: in view space, in volumetric space, and in UV (or texture) space. The integration of these two techniques achieves a win rate of 68% with respect to the single-stage model. We compare 3DGen to numerous industry baselines, and show that it outperforms them in terms of prompt fidelity and visual quality for complex textual prompts, while being significantly faster.
|
http://arxiv.org/pdf/2407.02599v1
|
[
"Raphael Bensadoun",
"Tom Monnier",
"Yanir Kleiman",
"Filippos Kokkinos",
"Yawar Siddiqui",
"Mahendra Kariya",
"Omri Harosh",
"Roman Shapovalov",
"Benjamin Graham",
"Emilien Garreau",
"Animesh Karnewar",
"Ang Cao",
"Idan Azuri",
"Iurii Makarov",
"Eric-Tuan Le",
"Antoine Toisoul",
"David Novotny",
"Oran Gafni",
"Natalia Neverova",
"Andrea Vedaldi"
] |
2024-07-02T18:37:52Z
|
2024-07-02T18:37:52Z
|
2407.02596
|
Towards More Realistic Extraction Attacks: An Adversarial Perspective
|
Language models are prone to memorizing large parts of their training data, making them vulnerable to extraction attacks. Existing research on these attacks remains limited in scope, often studying isolated trends rather than the real-world interactions with these models. In this paper, we revisit extraction attacks from an adversarial perspective, exploiting the brittleness of language models. We find significant churn in extraction attack trends, i.e., even minor, unintuitive changes to the prompt, or targeting smaller models and older checkpoints, can exacerbate the risks of extraction by up to $2-4 times$. Moreover, relying solely on the widely accepted verbatim match underestimates the extent of extracted information, and we provide various alternatives to more accurately capture the true risks of extraction. We conclude our discussion with data deduplication, a commonly suggested mitigation strategy, and find that while it addresses some memorization concerns, it remains vulnerable to the same escalation of extraction risks against a real-world adversary. Our findings highlight the necessity of acknowledging an adversary's true capabilities to avoid underestimating extraction risks.
|
http://arxiv.org/pdf/2407.02596v1
|
[
"Yash More",
"Prakhar Ganesh",
"Golnoosh Farnadi"
] |
2024-07-02T18:33:49Z
|
2024-07-02T18:33:49Z
|
2402.05359
|
An Examination on the Effectiveness of Divide-and-Conquer Prompting in
Large Language Models
|
Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications. However, when handling tasks involving repetitive sub-tasks and/or deceptive contents, such as arithmetic calculation and article-level fake news detection, simple instructional prompts suffer from inaccurate responses. Existing works show that more complicated prompting strategies, such as Chain-of-Thoughts and Least-to-Most, can unlock LLM's powerful capacity in diverse areas. Recent researches reveal that simple divide-and-conquer prompting strategy, i.e. simply dividing the input sequence to multiple sub-inputs, can also substantially improve LLM's performance in some specific tasks such as misinformation detection. In this paper, we aim at examining the utility of divide-and-conquer prompting strategy and answer on which kind of tasks this strategy gets advantages. Specifically, we provide a theoretic analysis to divide-and-conquer prompting strategy and help us identify the specific tasks where DaC prompting can bring performance boost with theoretic guarantee. We then present two cases (large integer arithmetic and fact verification) where experimental results aligns with our theoretic analysis.
|
http://arxiv.org/pdf/2402.05359v6
|
[
"Yizhou Zhang",
"Lun Du",
"Defu Cao",
"Qiang Fu",
"Yan Liu"
] |
2024-07-02T18:18:18Z
|
2024-02-08T02:37:30Z
|
2406.18794
|
Operator Learning of Lipschitz Operators: An Information-Theoretic
Perspective
|
Operator learning based on neural operators has emerged as a promising paradigm for the data-driven approximation of operators, mapping between infinite-dimensional Banach spaces. Despite significant empirical progress, our theoretical understanding regarding the efficiency of these approximations remains incomplete. This work addresses the parametric complexity of neural operator approximations for the general class of Lipschitz continuous operators. Motivated by recent findings on the limitations of specific architectures, termed curse of parametric complexity, we here adopt an information-theoretic perspective. Our main contribution establishes lower bounds on the metric entropy of Lipschitz operators in two approximation settings; uniform approximation over a compact set of input functions, and approximation in expectation, with input functions drawn from a probability measure. It is shown that these entropy bounds imply that, regardless of the activation function used, neural operator architectures attaining an approximation accuracy $epsilon$ must have a size that is exponentially large in $epsilon^{-1}$. The size of architectures is here measured by counting the number of encoded bits necessary to store the given model in computational memory. The results of this work elucidate fundamental trade-offs and limitations in operator learning.
|
http://arxiv.org/pdf/2406.18794v2
|
[
"Samuel Lanthaler"
] |
2024-07-02T18:13:03Z
|
2024-06-26T23:36:46Z
|
2212.04371
|
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with
Differential Privacy
|
Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train models with differential privacy, which provides rigorous privacy guarantees by injecting random noise to the gradients. This paper focuses on the scenario where sensitive data are distributed among multiple participants, who jointly train a model through federated learning (FL), using both secure multiparty computation (MPC) to ensure the confidentiality of each gradient update, and differential privacy to avoid data leakage in the resulting model. A major challenge in this setting is that common mechanisms for enforcing DP in deep learning, which inject real-valued noise, are fundamentally incompatible with MPC, which exchanges finite-field integers among the participants. Consequently, most existing DP mechanisms require rather high noise levels, leading to poor model utility. Motivated by this, we propose Skellam mixture mechanism (SMM), an approach to enforce DP on models built via FL. Compared to existing methods, SMM eliminates the assumption that the input gradients must be integer-valued, and, thus, reduces the amount of noise injected to preserve DP. Further, SMM allows tight privacy accounting due to the nice composition and sub-sampling properties of the Skellam distribution, which are key to accurate deep learning with DP. The theoretical analysis of SMM is highly non-trivial, especially considering (i) the complicated math of differentially private deep learning in general and (ii) the fact that the mixture of two Skellam distributions is rather complex, and to our knowledge, has not been studied in the DP literature. Extensive experiments on various practical settings demonstrate that SMM consistently and significantly outperforms existing solutions in terms of the utility of the resulting model.
|
http://arxiv.org/pdf/2212.04371v2
|
[
"Ergute Bao",
"Yizheng Zhu",
"Xiaokui Xiao",
"Yin Yang",
"Beng Chin Ooi",
"Benjamin Hong Meng Tan",
"Khin Mi Mi Aung"
] |
2024-07-02T18:03:28Z
|
2022-12-08T16:13:35Z
|
2407.02490
|
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via
Dynamic Sparse Attention
|
The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30 minutes for an 8B LLM to process a prompt of 1M tokens (i.e., the pre-filling stage) on a single A100 GPU. Existing methods for speeding up prefilling often fail to maintain acceptable accuracy or efficiency when applied to long-context LLMs. To address this gap, we introduce MInference (Milliontokens Inference), a sparse calculation method designed to accelerate pre-filling of long-sequence processing. Specifically, we identify three unique patterns in long-context attention matrices-the A-shape, Vertical-Slash, and Block-Sparsethat can be leveraged for efficient sparse computation on GPUs. We determine the optimal pattern for each attention head offline and dynamically build sparse indices based on the assigned pattern during inference. With the pattern and sparse indices, we perform efficient sparse attention calculations via our optimized GPU kernels to significantly reduce the latency in the pre-filling stage of long-context LLMs. Our proposed technique can be directly applied to existing LLMs without any modifications to the pre-training setup or additional fine-tuning. By evaluating on a wide range of downstream tasks, including InfiniteBench, RULER, PG-19, and Needle In A Haystack, and models including LLaMA-3-1M, GLM4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K, we demonstrate that MInference effectively reduces inference latency by up to 10x for pre-filling on an A100, while maintaining accuracy. Our code is available at https://aka.ms/MInference.
|
http://arxiv.org/pdf/2407.02490v1
|
[
"Huiqiang Jiang",
"Yucheng Li",
"Chengruidong Zhang",
"Qianhui Wu",
"Xufang Luo",
"Surin Ahn",
"Zhenhua Han",
"Amir H. Abdi",
"Dongsheng Li",
"Chin-Yew Lin",
"Yuqing Yang",
"Lili Qiu"
] |
2024-07-02T17:59:56Z
|
2024-07-02T17:59:56Z
|
2407.02489
|
Magic Insert: Style-Aware Drag-and-Drop
|
We present Magic Insert, a method for dragging-and-dropping subjects from a user-provided image into a target image of a different style in a physically plausible manner while matching the style of the target image. This work formalizes the problem of style-aware drag-and-drop and presents a method for tackling it by addressing two sub-problems: style-aware personalization and realistic object insertion in stylized images. For style-aware personalization, our method first fine-tunes a pretrained text-to-image diffusion model using LoRA and learned text tokens on the subject image, and then infuses it with a CLIP representation of the target style. For object insertion, we use Bootstrapped Domain Adaption to adapt a domain-specific photorealistic object insertion model to the domain of diverse artistic styles. Overall, the method significantly outperforms traditional approaches such as inpainting. Finally, we present a dataset, SubjectPlop, to facilitate evaluation and future progress in this area. Project page: https://magicinsert.github.io/
|
http://arxiv.org/pdf/2407.02489v1
|
[
"Nataniel Ruiz",
"Yuanzhen Li",
"Neal Wadhwa",
"Yael Pritch",
"Michael Rubinstein",
"David E. Jacobs",
"Shlomi Fruchter"
] |
2024-07-02T17:59:50Z
|
2024-07-02T17:59:50Z
|
2407.02486
|
Neurocache: Efficient Vector Retrieval for Long-range Language Modeling
|
This paper introduces Neurocache, an approach to extend the effective context size of large language models (LLMs) using an external vector cache to store its past states. Like recent vector retrieval approaches, Neurocache uses an efficient k-nearest-neighbor (kNN) algorithm to retrieve relevant past states and incorporate them into the attention process. Neurocache improves upon previous methods by (1) storing compressed states, which reduces cache size; (2) performing a single retrieval operation per token which increases inference speed; and (3) extending the retrieval window to neighboring states, which improves both language modeling and downstream task accuracy. Our experiments show the effectiveness of Neurocache both for models trained from scratch and for pre-trained models such as Llama2-7B and Mistral-7B when enhanced with the cache mechanism. We also compare Neurocache with text retrieval methods and show improvements in single-document question-answering and few-shot learning tasks. We made the source code available under: https://github.com/alisafaya/neurocache
|
http://arxiv.org/pdf/2407.02486v1
|
[
"Ali Safaya",
"Deniz Yuret"
] |
2024-07-02T17:59:29Z
|
2024-07-02T17:59:29Z
|
2407.02485
|
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in
LLMs
|
Large language models (LLMs) typically utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG). In this work, we propose a novel instruction fine-tuning framework RankRAG, which instruction-tunes a single LLM for the dual purpose of context ranking and answer generation in RAG. In particular, the instruction-tuned LLMs work surprisingly well by adding a small fraction of ranking data into the training blend, and outperform existing expert ranking models, including the same LLM exclusively fine-tuned on a large amount of ranking data. For generation, we compare our model with many strong baselines, including GPT-4-0613, GPT-4-turbo-2024-0409, and ChatQA-1.5, an open-sourced model with the state-of-the-art performance on RAG benchmarks. Specifically, our Llama3-RankRAG significantly outperforms Llama3-ChatQA-1.5 and GPT-4 models on nine knowledge-intensive benchmarks. In addition, it also performs comparably to GPT-4 on five RAG benchmarks in the biomedical domain without instruction fine-tuning on biomedical data, demonstrating its superb capability for generalization to new domains.
|
http://arxiv.org/pdf/2407.02485v1
|
[
"Yue Yu",
"Wei Ping",
"Zihan Liu",
"Boxin Wang",
"Jiaxuan You",
"Chao Zhang",
"Mohammad Shoeybi",
"Bryan Catanzaro"
] |
2024-07-02T17:59:17Z
|
2024-07-02T17:59:17Z
|
2407.02476
|
Scalable Multi-Output Gaussian Processes with Stochastic Variational
Inference
|
The Multi-Output Gaussian Process is is a popular tool for modelling data from multiple sources. A typical choice to build a covariance function for a MOGP is the Linear Model of Coregionalization (LMC) which parametrically models the covariance between outputs. The Latent Variable MOGP (LV-MOGP) generalises this idea by modelling the covariance between outputs using a kernel applied to latent variables, one per output, leading to a flexible MOGP model that allows efficient generalization to new outputs with few data points. Computational complexity in LV-MOGP grows linearly with the number of outputs, which makes it unsuitable for problems with a large number of outputs. In this paper, we propose a stochastic variational inference approach for the LV-MOGP that allows mini-batches for both inputs and outputs, making computational complexity per training iteration independent of the number of outputs.
|
http://arxiv.org/pdf/2407.02476v1
|
[
"Xiaoyu Jiang",
"Sokratia Georgaka",
"Magnus Rattray",
"Mauricio A. Alvarez"
] |
2024-07-02T17:53:56Z
|
2024-07-02T17:53:56Z
|
2407.03379
|
missForestPredict -- Missing data imputation for prediction settings
|
Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. The algorithm iteratively imputes variables using random forests until a convergence criterion (unified for continuous and categorical variables and based on the out-of-bag error) is met. The imputation models are saved for each variable and iteration and can be applied later to new observations at prediction time. The missForestPredict package offers extended error monitoring, control over variables used in the imputation and custom initialization. This allows users to tailor the imputation to their specific needs. The missForestPredict algorithm is compared to mean/mode imputation, linear regression imputation, mice, k-nearest neighbours, bagging, miceRanger and IterativeImputer on eight simulated datasets with simulated missingness (48 scenarios) and eight large public datasets using different prediction models. missForestPredict provides competitive results in prediction settings within short computation times.
|
http://arxiv.org/pdf/2407.03379v1
|
[
"Elena Albu",
"Shan Gao",
"Laure Wynants",
"Ben Van Calster"
] |
2024-07-02T17:45:46Z
|
2024-07-02T17:45:46Z
|
2407.02552
|
RLHF Can Speak Many Languages: Unlocking Multilingual Preference
Optimization for LLMs
|
Preference optimization techniques have become a standard final stage for training state-of-art large language models (LLMs). However, despite widespread adoption, the vast majority of work to-date has focused on first-class citizen languages like English and Chinese. This captures a small fraction of the languages in the world, but also makes it unclear which aspects of current state-of-the-art research transfer to a multilingual setting. In this work, we perform an exhaustive study to achieve a new state-of-the-art in aligning multilingual LLMs. We introduce a novel, scalable method for generating high-quality multilingual feedback data to balance data coverage. We establish the benefits of cross-lingual transfer and increased dataset size in preference training. Our preference-trained model achieves a 54.4% win-rate against Aya 23 8B, the current state-of-the-art multilingual LLM in its parameter class, and a 69.5% win-rate or higher against widely used models like Gemma-1.1-7B-it, Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.3. As a result of our study, we expand the frontier of alignment techniques to 23 languages covering half of the world's population.
|
http://arxiv.org/pdf/2407.02552v1
|
[
"John Dang",
"Arash Ahmadian",
"Kelly Marchisio",
"Julia Kreutzer",
"Ahmet Üstün",
"Sara Hooker"
] |
2024-07-02T17:42:30Z
|
2024-07-02T17:42:30Z
|
2407.02461
|
Decentralized Intelligence Network (DIN)
|
Decentralized Intelligence Network (DIN) addresses the significant challenges of data sovereignty and AI utilization caused by the fragmentation and siloing of data across providers and institutions. This comprehensive framework overcomes access barriers to scalable data sources previously hindered by silos by leveraging: 1) personal data stores as a prerequisite for data sovereignty; 2) a scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where data remains with participants and only model parameter updates are shared; and 3) a scalable, trustless rewards mechanism to incentivize participation and ensure fair reward distribution. This framework ensures that no entity can prevent or control access to training on data offered by participants or determine financial benefits, as these processes operate on a public blockchain with an immutable record and without a third party. It supports effective AI training, allowing participants to maintain control over their data, benefit financially, and contribute to a decentralized, scalable ecosystem that leverages collective AI to develop beneficial algorithms.
|
http://arxiv.org/pdf/2407.02461v1
|
[
"Abraham Nash"
] |
2024-07-02T17:40:06Z
|
2024-07-02T17:40:06Z
|
2402.05137
|
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics
and Cosmology
|
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schemata, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable and is designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterizing progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili.
|
http://arxiv.org/abs/2402.05137v2
|
[
"Matthew Ho",
"Deaglan J. Bartlett",
"Nicolas Chartier",
"Carolina Cuesta-Lazaro",
"Simon Ding",
"Axel Lapel",
"Pablo Lemos",
"Christopher C. Lovell",
"T. Lucas Makinen",
"Chirag Modi",
"Viraj Pandya",
"Shivam Pandey",
"Lucia A. Perez",
"Benjamin Wandelt",
"Greg L. Bryan"
] |
2024-07-02T17:38:18Z
|
2024-02-06T19:00:00Z
|
2402.17570
|
Sparse Variational Contaminated Noise Gaussian Process Regression with
Applications in Geomagnetic Perturbations Forecasting
|
Gaussian Processes (GP) have become popular machine-learning methods for kernel-based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood function to better account for heteroscedastic variance and outlier noise. We propose a scalable inference algorithm based on the Sparse Variational Gaussian Process (SVGP) method for fitting sparse Gaussian process regression models with contaminated normal noise on large datasets. We examine an application to geomagnetic ground perturbations, where the state-of-the-art prediction model is based on neural networks. We show that our approach yields shorter prediction intervals for similar coverage and accuracy when compared to an artificial dense neural network baseline.
|
http://arxiv.org/pdf/2402.17570v3
|
[
"Daniel Iong",
"Matthew McAnear",
"Yuezhou Qu",
"Shasha Zou",
"Gabor Toth",
"Yang Chen"
] |
2024-07-02T17:25:19Z
|
2024-02-27T15:08:57Z
|
2407.02447
|
PLeaS -- Merging Models with Permutations and Least Squares
|
The democratization of machine learning systems has made the process of fine-tuning accessible to a large number of practitioners, leading to a wide range of open-source models fine-tuned on specialized tasks and datasets. Recent work has proposed to merge such models to combine their functionalities. However, prior approaches are restricted to models that are fine-tuned from the same base model. Furthermore, the final merged model is typically restricted to be of the same size as the original models. In this work, we propose a new two-step algorithm to merge models-termed PLeaS-which relaxes these constraints. First, leveraging the Permutation symmetries inherent in the two models, PLeaS partially matches nodes in each layer by maximizing alignment. Next, PLeaS computes the weights of the merged model as a layer-wise Least Squares solution to minimize the approximation error between the features of the merged model and the permuted features of the original models. into a single model of a desired size, even when the two original models are fine-tuned from different base models. We also present a variant of our method which can merge models without using data from the fine-tuning domains. We demonstrate our method to merge ResNet models trained with shared and different label spaces, and show that we can perform better than the state-of-the-art merging methods by 8 to 15 percentage points for the same target compute while merging models trained on DomainNet and on fine-grained classification tasks.
|
http://arxiv.org/pdf/2407.02447v1
|
[
"Anshul Nasery",
"Jonathan Hayase",
"Pang Wei Koh",
"Sewoong Oh"
] |
2024-07-02T17:24:04Z
|
2024-07-02T17:24:04Z
|
2407.02437
|
Parameter Matching Attack: Enhancing Practical Applicability of
Availability Attacks
|
The widespread use of personal data for training machine learning models raises significant privacy concerns, as individuals have limited control over how their public data is subsequently utilized. Availability attacks have emerged as a means for data owners to safeguard their data by desning imperceptible perturbations that degrade model performance when incorporated into training datasets. However, existing availability attacks exhibit limitations in practical applicability, particularly when only a portion of the data can be perturbed. To address this challenge, we propose a novel availability attack approach termed Parameter Matching Attack (PMA). PMA is the first availability attack that works when only a portion of data can be perturbed. PMA optimizes perturbations so that when the model is trained on a mixture of clean and perturbed data, the resulting model will approach a model designed to perform poorly. Experimental results across four datasets demonstrate that PMA outperforms existing methods, achieving significant model performance degradation when a part of the training data is perturbed. Our code is available in the supplementary.
|
http://arxiv.org/pdf/2407.02437v1
|
[
"Yu Zhe",
"Jun Sakuma"
] |
2024-07-02T17:15:12Z
|
2024-07-02T17:15:12Z
|
2407.02432
|
Evaluating the Robustness of Adverse Drug Event Classification Models
Using Templates
|
An adverse drug effect (ADE) is any harmful event resulting from medical drug treatment. Despite their importance, ADEs are often under-reported in official channels. Some research has therefore turned to detecting discussions of ADEs in social media. Impressive results have been achieved in various attempts to detect ADEs. In a high-stakes domain such as medicine, however, an in-depth evaluation of a model's abilities is crucial. We address the issue of thorough performance evaluation in English-language ADE detection with hand-crafted templates for four capabilities: Temporal order, negation, sentiment, and beneficial effect. We find that models with similar performance on held-out test sets have varying results on these capabilities.
|
http://arxiv.org/pdf/2407.02432v1
|
[
"Dorothea MacPhail",
"David Harbecke",
"Lisa Raithel",
"Sebastian Möller"
] |
2024-07-02T17:09:24Z
|
2024-07-02T17:09:24Z
|
2407.02430
|
Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D
Objects
|
The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for 3D objects. Although recent texture generation methods achieve impressive results by using text-to-image networks, the combination of global consistency, quality, and speed, which is crucial for advancing texture generation to real-world applications, remains elusive. To that end, we introduce Meta 3D TextureGen: a new feedforward method comprised of two sequential networks aimed at generating high-quality and globally consistent textures for arbitrary geometries of any complexity degree in less than 20 seconds. Our method achieves state-of-the-art results in quality and speed by conditioning a text-to-image model on 3D semantics in 2D space and fusing them into a complete and high-resolution UV texture map, as demonstrated by extensive qualitative and quantitative evaluations. In addition, we introduce a texture enhancement network that is capable of up-scaling any texture by an arbitrary ratio, producing 4k pixel resolution textures.
|
http://arxiv.org/pdf/2407.02430v1
|
[
"Raphael Bensadoun",
"Yanir Kleiman",
"Idan Azuri",
"Omri Harosh",
"Andrea Vedaldi",
"Natalia Neverova",
"Oran Gafni"
] |
2024-07-02T17:04:34Z
|
2024-07-02T17:04:34Z
|
2009.10622
|
Non-asymptotic oracle inequalities for the Lasso in high-dimensional
mixture of experts
|
We investigate the estimation properties of the mixture of experts (MoE) model in a high-dimensional setting, where the number of predictors is much larger than the sample size, and for which the literature is particularly lacking in theoretical results. We consider the class of softmax-gated Gaussian MoE (SGMoE) models, defined as MoE models with softmax gating functions and Gaussian experts, and focus on the theoretical properties of their $l_1$-regularized estimation via the Lasso. To the best of our knowledge, we are the first to investigate the $l_1$-regularization properties of SGMoE models from a non-asymptotic perspective, under the mildest assumptions, namely the boundedness of the parameter space. We provide a lower bound on the regularization parameter of the Lasso penalty that ensures non-asymptotic theoretical control of the Kullback--Leibler loss of the Lasso estimator for SGMoE models. Finally, we carry out a simulation study to empirically validate our theoretical findings.
|
http://arxiv.org/pdf/2009.10622v7
|
[
"TrungTin Nguyen",
"Hien D Nguyen",
"Faicel Chamroukhi",
"Geoffrey J McLachlan"
] |
2024-07-02T17:04:08Z
|
2020-09-22T15:23:35Z
|
2007.03451
|
Analytics of Longitudinal System Monitoring Data for Performance
Prediction
|
In recent years, several HPC facilities have started continuous monitoring of their systems and jobs to collect performance-related data for understanding performance and operational efficiency. Such data can be used to optimize the performance of individual jobs and the overall system by creating data-driven models that can predict the performance of jobs waiting in the scheduler queue. In this paper, we model the performance of representative control jobs using longitudinal system-wide monitoring data and machine learning to explore the causes of performance variability. We analyze these prediction models in great detail to identify the features that are dominant predictors of performance. We demonstrate that such models can be application-agnostic and can be used for predicting performance of applications that are not included in training.
|
http://arxiv.org/pdf/2007.03451v2
|
[
"Ian J. Costello",
"Abhinav Bhatele"
] |
2024-07-02T17:02:59Z
|
2020-07-07T13:57:59Z
|
2407.02428
|
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear
Transfer Function Identifications
|
The control and modeling of bionic robot dynamics have increasingly adopted model-free control strategies using machine learning methods. Given the non-linear elastic nature of bionic robotic systems, learning-based methods provide reliable alternatives by utilizing numerical data to establish a direct mapping from actuation inputs to robot trajectories without complex kinematics models. However, for developers, the method of identifying an appropriate learning model for their specific bionic robots and further constructing the transfer function has not been thoroughly discussed. Thus, this research trains four types of models, including ensemble learning models, regularization-based models, kernel-based models, and neural network models, suitable for multi-input multi-output (MIMO) data and non-linear transfer function identification, in order to evaluate their (1) accuracy, (2) computation complexity, and (3) performance of capturing biological movements. This research encompasses data collection methods for control inputs and action outputs, selection of machine learning models, comparative analysis of training results, and transfer function identifications. The main objective is to provide a comprehensive evaluation strategy and framework for the application of model-free control.
|
http://arxiv.org/pdf/2407.02428v1
|
[
"Po-Yu Hsieh",
"June-Hao Hou"
] |
2024-07-02T17:00:23Z
|
2024-07-02T17:00:23Z
|
2310.10958
|
Enhancing Deep Neural Network Training Efficiency and Performance
through Linear Prediction
|
Deep neural networks (DNN) have achieved remarkable success in various fields, including computer vision and natural language processing. However, training an effective DNN model still poses challenges. This paper aims to propose a method to optimize the training effectiveness of DNN, with the goal of improving model performance. Firstly, based on the observation that the DNN parameters change in certain laws during training process, the potential of parameter prediction for improving model training efficiency and performance is discovered. Secondly, considering the magnitude of DNN model parameters, hardware limitations and characteristics of Stochastic Gradient Descent (SGD) for noise tolerance, a Parameter Linear Prediction (PLP) method is exploit to perform DNN parameter prediction. Finally, validations are carried out on some representative backbones. Experiment results show that compare to the normal training ways, under the same training conditions and epochs, by employing proposed PLP method, the optimal model is able to obtain average about 1% accuracy improvement and 0.01 top-1/top-5 error reduction for Vgg16, Resnet18 and GoogLeNet based on CIFAR-100 dataset, which shown the effectiveness of the proposed method on different DNN structures, and validated its capacity in enhancing DNN training efficiency and performance.
|
http://arxiv.org/pdf/2310.10958v2
|
[
"Hejie Ying",
"Mengmeng Song",
"Yaohong Tang",
"Shungen Xiao",
"Zimin Xiao"
] |
2024-07-02T16:57:06Z
|
2023-10-17T03:11:30Z
|
2403.06659
|
Zero-Shot ECG Classification with Multimodal Learning and Test-time
Clinical Knowledge Enhancement
|
Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) methods show promise in representation learning from unannotated ECG data, they often overlook the clinical knowledge that can be found in reports. This oversight and the requirement for annotated samples for downstream tasks limit eSSL's versatility. In this work, we address these issues with the Multimodal ECG Representation Learning (MERL}) framework. Through multimodal learning on ECG records and associated reports, MERL is capable of performing zero-shot ECG classification with text prompts, eliminating the need for training data in downstream tasks. At test time, we propose the Clinical Knowledge Enhanced Prompt Engineering (CKEPE) approach, which uses Large Language Models (LLMs) to exploit external expert-verified clinical knowledge databases, generating more descriptive prompts and reducing hallucinations in LLM-generated content to boost zero-shot classification. Based on MERL, we perform the first benchmark across six public ECG datasets, showing the superior performance of MERL compared against eSSL methods. Notably, MERL achieves an average AUC score of 75.2% in zero-shot classification (without training data), 3.2% higher than linear probed eSSL methods with 10% annotated training data, averaged across all six datasets. Code and models are available at https://github.com/cheliu-computation/MERL
|
http://arxiv.org/pdf/2403.06659v3
|
[
"Che Liu",
"Zhongwei Wan",
"Cheng Ouyang",
"Anand Shah",
"Wenjia Bai",
"Rossella Arcucci"
] |
2024-07-02T16:51:11Z
|
2024-03-11T12:28:55Z
|
2407.02424
|
A Pattern Language for Machine Learning Tasks
|
Idealised as universal approximators, learners such as neural networks can be viewed as "variable functions" that may become one of a range of concrete functions after training. In the same way that equations constrain the possible values of variables in algebra, we may view objective functions as constraints on the behaviour of learners. We extract the equivalences perfectly optimised objective functions impose, calling them "tasks". For these tasks, we develop a formal graphical language that allows us to: (1) separate the core tasks of a behaviour from its implementation details; (2) reason about and design behaviours model-agnostically; and (3) simply describe and unify approaches in machine learning across domains. As proof-of-concept, we design a novel task that enables converting classifiers into generative models we call "manipulators", which we implement by directly translating task specifications into code. The resulting models exhibit capabilities such as style transfer and interpretable latent-space editing, without the need for custom architectures, adversarial training or random sampling. We formally relate the behaviour of manipulators to GANs, and empirically demonstrate their competitive performance with VAEs. We report on experiments across vision and language domains aiming to characterise manipulators as approximate Bayesian inversions of discriminative classifiers.
|
http://arxiv.org/pdf/2407.02424v1
|
[
"Benjamin Rodatz",
"Ian Fan",
"Tuomas Laakkonen",
"Neil John Ortega",
"Thomas Hoffman",
"Vincent Wang-Mascianica"
] |
2024-07-02T16:50:27Z
|
2024-07-02T16:50:27Z
|
2406.17523
|
On the consistency of hyper-parameter selection in value-based deep
reinforcement learning
|
Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and careful selection of hyper-parameters. Algorithmic improvements are often the result of iterative enhancements built upon prior approaches, while hyper-parameter choices are typically inherited from previous methods or fine-tuned specifically for the proposed technique. Despite their crucial impact on performance, hyper-parameter choices are frequently overshadowed by algorithmic advancements. This paper conducts an extensive empirical study focusing on the reliability of hyper-parameter selection for value-based deep reinforcement learning agents, including the introduction of a new score to quantify the consistency and reliability of various hyper-parameters. Our findings not only help establish which hyper-parameters are most critical to tune, but also help clarify which tunings remain consistent across different training regimes.
|
http://arxiv.org/pdf/2406.17523v2
|
[
"Johan Obando-Ceron",
"João G. M. Araújo",
"Aaron Courville",
"Pablo Samuel Castro"
] |
2024-07-02T16:33:26Z
|
2024-06-25T13:06:09Z
|
2407.02408
|
CEB: Compositional Evaluation Benchmark for Fairness in Large Language
Models
|
As Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks, concerns regarding the potential negative societal impacts of LLM-generated content have also arisen. To evaluate the biases exhibited by LLMs, researchers have recently proposed a variety of datasets. However, existing bias evaluation efforts often focus on only a particular type of bias and employ inconsistent evaluation metrics, leading to difficulties in comparison across different datasets and LLMs. To address these limitations, we collect a variety of datasets designed for the bias evaluation of LLMs, and further propose CEB, a Compositional Evaluation Benchmark that covers different types of bias across different social groups and tasks. The curation of CEB is based on our newly proposed compositional taxonomy, which characterizes each dataset from three dimensions: bias types, social groups, and tasks. By combining the three dimensions, we develop a comprehensive evaluation strategy for the bias in LLMs. Our experiments demonstrate that the levels of bias vary across these dimensions, thereby providing guidance for the development of specific bias mitigation methods.
|
http://arxiv.org/pdf/2407.02408v1
|
[
"Song Wang",
"Peng Wang",
"Tong Zhou",
"Yushun Dong",
"Zhen Tan",
"Jundong Li"
] |
2024-07-02T16:31:37Z
|
2024-07-02T16:31:37Z
|
2407.02405
|
Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter
Inference on Multi-Tasking Autonomous Nano-Drones
|
Pocket-sized autonomous nano-drones can revolutionize many robotic use cases, such as visual inspection in narrow, constrained spaces, and ensure safer human-robot interaction due to their tiny form factor and weight -- i.e., tens of grams. This compelling vision is challenged by the high level of intelligence needed aboard, which clashes against the limited computational and storage resources available on PULP (parallel-ultra-low-power) MCU class navigation and mission controllers that can be hosted aboard. This work moves from PULP-Dronet, a State-of-the-Art convolutional neural network for autonomous navigation on nano-drones. We introduce Tiny-PULP-Dronet: a novel methodology to squeeze by more than one order of magnitude model size (50x fewer parameters), and number of operations (27x less multiply-and-accumulate) required to run inference with similar flight performance as PULP-Dronet. This massive reduction paves the way towards affordable multi-tasking on nano-drones, a fundamental requirement for achieving high-level intelligence.
|
http://arxiv.org/pdf/2407.02405v1
|
[
"Lorenzo Lamberti",
"Vlad Niculescu",
"Michał Barcis",
"Lorenzo Bellone",
"Enrico Natalizio",
"Luca Benini",
"Daniele Palossi"
] |
2024-07-02T16:24:57Z
|
2024-07-02T16:24:57Z
|
2406.16976
|
Efficient Evolutionary Search Over Chemical Space with Large Language
Models
|
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutations and crossovers, leading to a large number of expensive objective evaluations. In this work, we ameliorate this shortcoming by incorporating chemistry-aware Large Language Models (LLMs) into EAs. Namely, we redesign crossover and mutation operations in EAs using LLMs trained on large corpora of chemical information. We perform extensive empirical studies on both commercial and open-source models on multiple tasks involving property optimization, molecular rediscovery, and structure-based drug design, demonstrating that the joint usage of LLMs with EAs yields superior performance over all baseline models across single- and multi-objective settings. We demonstrate that our algorithm improves both the quality of the final solution and convergence speed, thereby reducing the number of required objective evaluations. Our code is available at http://github.com/zoom-wang112358/MOLLEO
|
http://arxiv.org/pdf/2406.16976v2
|
[
"Haorui Wang",
"Marta Skreta",
"Cher-Tian Ser",
"Wenhao Gao",
"Lingkai Kong",
"Felix Strieth-Kalthoff",
"Chenru Duan",
"Yuchen Zhuang",
"Yue Yu",
"Yanqiao Zhu",
"Yuanqi Du",
"Alán Aspuru-Guzik",
"Kirill Neklyudov",
"Chao Zhang"
] |
2024-07-02T16:12:38Z
|
2024-06-23T06:22:49Z
|
2407.02390
|
Uncertainty-Aware Decarbonization for Datacenters
|
This paper represents the first effort to quantify uncertainty in carbon intensity forecasting for datacenter decarbonization. We identify and analyze two types of uncertainty -- temporal and spatial -- and discuss their system implications. To address the temporal dynamics in quantifying uncertainty for carbon intensity forecasting, we introduce a conformal prediction-based framework. Evaluation results show that our technique robustly achieves target coverages in uncertainty quantification across various significance levels. We conduct two case studies using production power traces, focusing on temporal and spatial load shifting respectively. The results show that incorporating uncertainty into scheduling decisions can prevent a 5% and 14% increase in carbon emissions, respectively. These percentages translate to an absolute reduction of 2.1 and 10.4 tons of carbon emissions in a 20 MW datacenter cluster.
|
http://arxiv.org/pdf/2407.02390v1
|
[
"Amy Li",
"Sihang Liu",
"Yi Ding"
] |
2024-07-02T16:04:16Z
|
2024-07-02T16:04:16Z
|
2309.14277
|
SINCERE: Supervised Information Noise-Contrastive Estimation REvisited
|
The information noise-contrastive estimation (InfoNCE) loss function provides the basis of many self-supervised deep learning methods due to its strong empirical results and theoretic motivation. Previous work suggests a supervised contrastive (SupCon) loss to extend InfoNCE to learn from available class labels. This SupCon loss has been widely-used due to reports of good empirical performance. However, in this work we find that the prior SupCon loss formulation has questionable justification because it can encourage some images from the same class to repel one another in the learned embedding space. This problematic intra-class repulsion gets worse as the number of images sharing one class label increases. We propose the Supervised InfoNCE REvisited (SINCERE) loss as a theoretically-justified supervised extension of InfoNCE that eliminates intra-class repulsion. Experiments show that SINCERE leads to better separation of embeddings from different classes and improves transfer learning classification accuracy. We additionally utilize probabilistic modeling to derive an information-theoretic bound that relates SINCERE loss to the symmeterized KL divergence between data-generating distributions for a target class and all other classes.
|
http://arxiv.org/pdf/2309.14277v3
|
[
"Patrick Feeney",
"Michael C. Hughes"
] |
2024-07-02T16:02:39Z
|
2023-09-25T16:40:56Z
|
2407.02389
|
SafaRi:Adaptive Sequence Transformer for Weakly Supervised Referring
Expression Segmentation
|
Referring Expression Segmentation (RES) aims to provide a segmentation mask of the target object in an image referred to by the text (i.e., referring expression). Existing methods require large-scale mask annotations. Moreover, such approaches do not generalize well to unseen/zero-shot scenarios. To address the aforementioned issues, we propose a weakly-supervised bootstrapping architecture for RES with several new algorithmic innovations. To the best of our knowledge, ours is the first approach that considers only a fraction of both mask and box annotations (shown in Figure 1 and Table 1) for training. To enable principled training of models in such low-annotation settings, improve image-text region-level alignment, and further enhance spatial localization of the target object in the image, we propose Cross-modal Fusion with Attention Consistency module. For automatic pseudo-labeling of unlabeled samples, we introduce a novel Mask Validity Filtering routine based on a spatially aware zero-shot proposal scoring approach. Extensive experiments show that with just 30% annotations, our model SafaRi achieves 59.31 and 48.26 mIoUs as compared to 58.93 and 48.19 mIoUs obtained by the fully-supervised SOTA method SeqTR respectively on RefCOCO+@testA and RefCOCO+testB datasets. SafaRi also outperforms SeqTR by 11.7% (on RefCOCO+testA) and 19.6% (on RefCOCO+testB) in a fully-supervised setting and demonstrates strong generalization capabilities in unseen/zero-shot tasks.
|
http://arxiv.org/pdf/2407.02389v1
|
[
"Sayan Nag",
"Koustava Goswami",
"Srikrishna Karanam"
] |
2024-07-02T16:02:25Z
|
2024-07-02T16:02:25Z
|
2207.12067
|
Homomorphism Autoencoder -- Learning Group Structured Representations
from Observed Transitions
|
How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations containing not just observational but also interventional knowledge, we study this problem using tools from representation learning and group theory. We propose methods enabling an agent acting upon the world to learn internal representations of sensory information that are consistent with actions that modify it. We use an autoencoder equipped with a group representation acting on its latent space, trained using an equivariance-derived loss in order to enforce a suitable homomorphism property on the group representation. In contrast to existing work, our approach does not require prior knowledge of the group and does not restrict the set of actions the agent can perform. We motivate our method theoretically, and show empirically that it can learn a group representation of the actions, thereby capturing the structure of the set of transformations applied to the environment. We further show that this allows agents to predict the effect of sequences of future actions with improved accuracy.
|
http://arxiv.org/pdf/2207.12067v3
|
[
"Hamza Keurti",
"Hsiao-Ru Pan",
"Michel Besserve",
"Benjamin F. Grewe",
"Bernhard Schölkopf"
] |
2024-07-02T15:46:13Z
|
2022-07-25T11:22:48Z
|
2407.02369
|
Two-Step Q-Learning
|
Q-learning is a stochastic approximation version of the classic value iteration. The literature has established that Q-learning suffers from both maximization bias and slower convergence. Recently, multi-step algorithms have shown practical advantages over existing methods. This paper proposes a novel off-policy two-step Q-learning algorithms, without importance sampling. With suitable assumption it was shown that, iterates in the proposed two-step Q-learning is bounded and converges almost surely to the optimal Q-values. This study also address the convergence analysis of the smooth version of two-step Q-learning, i.e., by replacing max function with the log-sum-exp function. The proposed algorithms are robust and easy to implement. Finally, we test the proposed algorithms on benchmark problems such as the roulette problem, maximization bias problem, and randomly generated Markov decision processes and compare it with the existing methods available in literature. Numerical experiments demonstrate the superior performance of both the two-step Q-learning and its smooth variants.
|
http://arxiv.org/pdf/2407.02369v1
|
[
"Antony Vijesh",
"Shreyas S R"
] |
2024-07-02T15:39:00Z
|
2024-07-02T15:39:00Z
|
2406.14325
|
Reproducibility in Machine Learning-based Research: Overview, Barriers
and Drivers
|
Research in various fields is currently experiencing challenges regarding the reproducibility of results. This problem is also prevalent in machine learning (ML) research. The issue arises, for example, due to unpublished data and/or source code and the sensitivity of ML training conditions. Although different solutions have been proposed to address this issue, such as using ML platforms, the level of reproducibility in ML-driven research remains unsatisfactory. Therefore, in this article, we discuss the reproducibility of ML-driven research with three main aims: (i) identifying the barriers to reproducibility when applying ML in research as well as categorize the barriers to different types of reproducibility (description, code, data, and experiment reproducibility), (ii) discussing potential drivers such as tools, practices, and interventions that support ML reproducibility, as well as distinguish between technology-driven drivers, procedural drivers, and drivers related to awareness and education, and (iii) mapping the drivers to the barriers. With this work, we hope to provide insights and to contribute to the decision-making process regarding the adoption of different solutions to support ML reproducibility.
|
http://arxiv.org/pdf/2406.14325v2
|
[
"Harald Semmelrock",
"Tony Ross-Hellauer",
"Simone Kopeinik",
"Dieter Theiler",
"Armin Haberl",
"Stefan Thalmann",
"Dominik Kowald"
] |
2024-07-02T15:36:32Z
|
2024-06-20T13:56:42Z
|
2407.02549
|
Diffusion Models for Tabular Data Imputation and Synthetic Data
Generation
|
Data imputation and data generation have important applications for many domains, like healthcare and finance, where incomplete or missing data can hinder accurate analysis and decision-making. Diffusion models have emerged as powerful generative models capable of capturing complex data distributions across various data modalities such as image, audio, and time series data. Recently, they have been also adapted to generate tabular data. In this paper, we propose a diffusion model for tabular data that introduces three key enhancements: (1) a conditioning attention mechanism, (2) an encoder-decoder transformer as the denoising network, and (3) dynamic masking. The conditioning attention mechanism is designed to improve the model's ability to capture the relationship between the condition and synthetic data. The transformer layers help model interactions within the condition (encoder) or synthetic data (decoder), while dynamic masking enables our model to efficiently handle both missing data imputation and synthetic data generation tasks within a unified framework. We conduct a comprehensive evaluation by comparing the performance of diffusion models with transformer conditioning against state-of-the-art techniques, such as Variational Autoencoders, Generative Adversarial Networks and Diffusion Models, on benchmark datasets. Our evaluation focuses on the assessment of the generated samples with respect to three important criteria, namely: (1) Machine Learning efficiency, (2) statistical similarity, and (3) privacy risk mitigation. For the task of data imputation, we consider the efficiency of the generated samples across different levels of missing features.
|
http://arxiv.org/pdf/2407.02549v1
|
[
"Mario Villaizán-Vallelado",
"Matteo Salvatori",
"Carlos Segura",
"Ioannis Arapakis"
] |
2024-07-02T15:27:06Z
|
2024-07-02T15:27:06Z
|
2407.02356
|
Enable the Right to be Forgotten with Federated Client Unlearning in
Medical Imaging
|
The right to be forgotten, as stated in most data regulations, poses an underexplored challenge in federated learning (FL), leading to the development of federated unlearning (FU). However, current FU approaches often face trade-offs between efficiency, model performance, forgetting efficacy, and privacy preservation. In this paper, we delve into the paradigm of Federated Client Unlearning (FCU) to guarantee a client the right to erase the contribution or the influence, introducing the first FU framework in medical imaging. In the unlearning process of a client, the proposed model-contrastive unlearning marks a pioneering step towards feature-level unlearning, and frequency-guided memory preservation ensures smooth forgetting of local knowledge while maintaining the generalizability of the trained global model, thus avoiding performance compromises and guaranteeing rapid post-training. We evaluated our FCU framework on two public medical image datasets, including Intracranial hemorrhage diagnosis and skin lesion diagnosis, demonstrating that our framework outperformed other state-of-the-art FU frameworks, with an expected speed-up of 10-15 times compared with retraining from scratch. The code and the organized datasets can be found at: https://github.com/dzp2095/FCU.
|
http://arxiv.org/pdf/2407.02356v1
|
[
"Zhipeng Deng",
"Luyang Luo",
"Hao Chen"
] |
2024-07-02T15:21:11Z
|
2024-07-02T15:21:11Z
|
2407.01458
|
Contractual Reinforcement Learning: Pulling Arms with Invisible Hands
|
The agency problem emerges in today's large scale machine learning tasks, where the learners are unable to direct content creation or enforce data collection. In this work, we propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. The problem, termed emph{contractual reinforcement learning}, naturally arises from the classic model of Markov decision processes, where a learning principal seeks to optimally influence the agent's action policy for their common interests through a set of payment rules contingent on the realization of next state. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation, reducing the complexity analysis to the construction of efficient search algorithms. For several natural classes of problems, we design tailored search algorithms that provably achieve $tilde{O}(sqrt{T})$ regret. We also present an algorithm with $tilde{O}(T^{2/3})$ for the general problem that improves the existing analysis in online contract design with mild technical assumptions.
|
http://arxiv.org/pdf/2407.01458v2
|
[
"Jibang Wu",
"Siyu Chen",
"Mengdi Wang",
"Huazheng Wang",
"Haifeng Xu"
] |
2024-07-02T15:17:50Z
|
2024-07-01T16:53:00Z
|
2407.02348
|
Revisiting Cascaded Ensembles for Efficient Inference
|
A common approach to make machine learning inference more efficient is to use example-specific adaptive schemes, which route or select models for each example at inference time. In this work we study a simple scheme for adaptive inference. We build a cascade of ensembles (CoE), beginning with resource-efficient models and growing to larger, more expressive models, where ensemble agreement serves as a data-dependent routing criterion. This scheme is easy to incorporate into existing inference pipelines, requires no additional training, and can be used to place models across multiple resource tiers--for instance, serving efficient models at the edge and invoking larger models in the cloud only when necessary. In cases where parallel inference is feasible, we show that CoE can improve accuracy relative to the single best model while reducing the average cost of inference by up to 7x, and provides Pareto-dominate solutions in accuracy and efficiency relative to existing adaptive inference baselines. These savings translate to an over 3x-reduction in total monetary cost when performing inference using a heterogeneous cluster of GPUs. Finally, for edge inference scenarios where portions of the cascade reside at the edge vs. in the cloud, CoE can provide a 14x reduction in communication cost and inference latency without sacrificing accuracy.
|
http://arxiv.org/pdf/2407.02348v1
|
[
"Steven Kolawole",
"Don Dennis",
"Ameet Talwalkar",
"Virginia Smith"
] |
2024-07-02T15:14:12Z
|
2024-07-02T15:14:12Z
|
2407.02335
|
CALICO: Confident Active Learning with Integrated Calibration
|
The growing use of deep learning in safety-critical applications, such as medical imaging, has raised concerns about limited labeled data, where this demand is amplified as model complexity increases, posing hurdles for domain experts to annotate data. In response to this, active learning (AL) is used to efficiently train models with limited annotation costs. In the context of deep neural networks (DNNs), AL often uses confidence or probability outputs as a score for selecting the most informative samples. However, modern DNNs exhibit unreliable confidence outputs, making calibration essential. We propose an AL framework that self-calibrates the confidence used for sample selection during the training process, referred to as Confident Active Learning with Integrated CalibratiOn (CALICO). CALICO incorporates the joint training of a classifier and an energy-based model, instead of the standard softmax-based classifier. This approach allows for simultaneous estimation of the input data distribution and the class probabilities during training, improving calibration without needing an additional labeled dataset. Experimental results showcase improved classification performance compared to a softmax-based classifier with fewer labeled samples. Furthermore, the calibration stability of the model is observed to depend on the prior class distribution of the data.
|
http://arxiv.org/pdf/2407.02335v1
|
[
"Lorenzo S. Querol",
"Hajime Nagahara",
"Hideaki Hayashi"
] |
2024-07-02T15:05:19Z
|
2024-07-02T15:05:19Z
|
2407.02327
|
QSync: Quantization-Minimized Synchronous Distributed Training Across
Hybrid Devices
|
A number of production deep learning clusters have attempted to explore inference hardware for DNN training, at the off-peak serving hours with many inference GPUs idling. Conducting DNN training with a combination of heterogeneous training and inference GPUs, known as hybrid device training, presents considerable challenges due to disparities in compute capability and significant differences in memory capacity. We propose QSync, a training system that enables efficient synchronous data-parallel DNN training over hybrid devices by strategically exploiting quantized operators. According to each device's available resource capacity, QSync selects a quantization-minimized setting for operators in the distributed DNN training graph, minimizing model accuracy degradation but keeping the training efficiency brought by quantization. We carefully design a predictor with a bi-directional mixed-precision indicator to reflect the sensitivity of DNN layers on fixed-point and floating-point low-precision operators, a replayer with a neighborhood-aware cost mapper to accurately estimate the latency of distributed hybrid mixed-precision training, and then an allocator that efficiently synchronizes workers with minimized model accuracy degradation. QSync bridges the computational graph on PyTorch to an optimized backend for quantization kernel performance and flexible support for various GPU architectures. Extensive experiments show that QSync's predictor can accurately simulate distributed mixed-precision training with <5% error, with a consistent 0.27-1.03% accuracy improvement over the from-scratch training tasks compared to uniform precision.
|
http://arxiv.org/pdf/2407.02327v1
|
[
"Juntao Zhao",
"Borui Wan",
"Yanghua Peng",
"Haibin Lin",
"Yibo Zhu",
"Chuan Wu"
] |
2024-07-02T14:56:47Z
|
2024-07-02T14:56:47Z
|
2407.02322
|
Stochastic Differential Equations models for Least-Squares Stochastic
Gradient Descent
|
We study the dynamics of a continuous-time model of the Stochastic Gradient Descent (SGD) for the least-square problem. Indeed, pursuing the work of Li et al. (2019), we analyze Stochastic Differential Equations (SDEs) that model SGD either in the case of the training loss (finite samples) or the population one (online setting). A key qualitative feature of the dynamics is the existence of a perfect interpolator of the data, irrespective of the sample size. In both scenarios, we provide precise, non-asymptotic rates of convergence to the (possibly degenerate) stationary distribution. Additionally, we describe this asymptotic distribution, offering estimates of its mean, deviations from it, and a proof of the emergence of heavy-tails related to the step-size magnitude. Numerical simulations supporting our findings are also presented.
|
http://arxiv.org/pdf/2407.02322v1
|
[
"Adrien Schertzer",
"Loucas Pillaud-Vivien"
] |
2024-07-02T14:52:21Z
|
2024-07-02T14:52:21Z
|
2403.07311
|
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
|
The task of multi-hop link prediction within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, as it requires the model to reason through and understand all intermediate connections before making a prediction. In this paper, we introduce the Knowledge Graph Large Language Model (KG-LLM), a novel framework that leverages large language models (LLMs) for knowledge graph tasks. We first convert structured knowledge graph data into natural language and then use these natural language prompts to fine-tune LLMs to enhance multi-hop link prediction in KGs. By converting the KG to natural language prompts, our framework is designed to learn the latent representations of entities and their interrelations. To show the efficacy of the KG-LLM Framework, we fine-tune three leading LLMs within this framework, including Flan-T5, LLaMa2 and Gemma. Further, we explore the framework's potential to provide LLMs with zero-shot capabilities for handling previously unseen prompts. Experimental results show that KG-LLM significantly improves the models' generalization capabilities, leading to more accurate predictions in unfamiliar scenarios.
|
http://arxiv.org/pdf/2403.07311v7
|
[
"Dong Shu",
"Tianle Chen",
"Mingyu Jin",
"Chong Zhang",
"Mengnan Du",
"Yongfeng Zhang"
] |
2024-07-02T14:52:16Z
|
2024-03-12T04:47:29Z
|
2407.02309
|
Semantically Guided Representation Learning For Action Anticipation
|
Action anticipation is the task of forecasting future activity from a partially observed sequence of events. However, this task is exposed to intrinsic future uncertainty and the difficulty of reasoning upon interconnected actions. Unlike previous works that focus on extrapolating better visual and temporal information, we concentrate on learning action representations that are aware of their semantic interconnectivity based on prototypical action patterns and contextual co-occurrences. To this end, we propose the novel Semantically Guided Representation Learning (S-GEAR) framework. S-GEAR learns visual action prototypes and leverages language models to structure their relationship, inducing semanticity. To gather insights on S-GEAR's effectiveness, we test it on four action anticipation benchmarks, obtaining improved results compared to previous works: +3.5, +2.7, and +3.5 absolute points on Top-1 Accuracy on Epic-Kitchen 55, EGTEA Gaze+ and 50 Salads, respectively, and +0.8 on Top-5 Recall on Epic-Kitchens 100. We further observe that S-GEAR effectively transfers the geometric associations between actions from language to visual prototypes. Finally, S-GEAR opens new research frontiers in anticipation tasks by demonstrating the intricate impact of action semantic interconnectivity.
|
http://arxiv.org/pdf/2407.02309v1
|
[
"Anxhelo Diko",
"Danilo Avola",
"Bardh Prenkaj",
"Federico Fontana",
"Luigi Cinque"
] |
2024-07-02T14:44:01Z
|
2024-07-02T14:44:01Z
|
2405.10938
|
Observational Scaling Laws and the Predictability of Language Model
Performance
|
Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but the requirement of training models across many different scales has limited their use. We propose an alternative, observational approach that bypasses model training and instead builds scaling laws from ~80 publically available models. Building a single scaling law from multiple model families is challenging due to large variations in their training compute efficiencies and capabilities. However, we show that these variations are consistent with a simple, generalized scaling law where language model performance is a function of a low-dimensional capability space, and model families only vary in their efficiency in converting training compute to capabilities. Using this approach, we show the surprising predictability of complex scaling phenomena: we show that several emergent phenomena follow a smooth, sigmoidal behavior and are predictable from small models; we show that the agent performance of models such as GPT-4 can be precisely predicted from simpler non-agentic benchmarks; and we show how to predict the impact of post-training interventions like Chain-of-Thought and Self-Consistency as language model capabilities continue to improve.
|
http://arxiv.org/pdf/2405.10938v2
|
[
"Yangjun Ruan",
"Chris J. Maddison",
"Tatsunori Hashimoto"
] |
2024-07-02T14:16:42Z
|
2024-05-17T17:49:44Z
|
2405.05512
|
Characteristic Learning for Provable One Step Generation
|
We propose the characteristic generator, a novel one-step generative model that combines the efficiency of sampling in Generative Adversarial Networks (GANs) with the stable performance of flow-based models. Our model is driven by characteristics, along which the probability density transport can be described by ordinary differential equations (ODEs). Specifically, We estimate the velocity field through nonparametric regression and utilize Euler method to solve the probability flow ODE, generating a series of discrete approximations to the characteristics. We then use a deep neural network to fit these characteristics, ensuring a one-step mapping that effectively pushes the prior distribution towards the target distribution. In the theoretical aspect, we analyze the errors in velocity matching, Euler discretization, and characteristic fitting to establish a non-asymptotic convergence rate for the characteristic generator in 2-Wasserstein distance. To the best of our knowledge, this is the first thorough analysis for simulation-free one step generative models. Additionally, our analysis refines the error analysis of flow-based generative models in prior works. We apply our method on both synthetic and real datasets, and the results demonstrate that the characteristic generator achieves high generation quality with just a single evaluation of neural network.
|
http://arxiv.org/pdf/2405.05512v3
|
[
"Zhao Ding",
"Chenguang Duan",
"Yuling Jiao",
"Ruoxuan Li",
"Jerry Zhijian Yang",
"Pingwen Zhang"
] |
2024-07-02T14:14:41Z
|
2024-05-09T02:41:42Z
|
2407.02279
|
How to Boost Any Loss Function
|
Boosting is a highly successful ML-born optimization setting in which one is required to computationally efficiently learn arbitrarily good models based on the access to a weak learner oracle, providing classifiers performing at least slightly differently from random guessing. A key difference with gradient-based optimization is that boosting's original model does not requires access to first order information about a loss, yet the decades long history of boosting has quickly evolved it into a first order optimization setting -- sometimes even wrongfully textit{defining} it as such. Owing to recent progress extending gradient-based optimization to use only a loss' zeroth ($0^{th}$) order information to learn, this begs the question: what loss functions can be efficiently optimized with boosting and what is the information really needed for boosting to meet the textit{original} boosting blueprint's requirements? We provide a constructive formal answer essentially showing that textit{any} loss function can be optimized with boosting and thus boosting can achieve a feat not yet known to be possible in the classical $0^{th}$ order setting, since loss functions are not required to be be convex, nor differentiable or Lipschitz -- and in fact not required to be continuous either. Some tools we use are rooted in quantum calculus, the mathematical field -- not to be confounded with quantum computation -- that studies calculus without passing to the limit, and thus without using first order information.
|
http://arxiv.org/pdf/2407.02279v1
|
[
"Richard Nock",
"Yishay Mansour"
] |
2024-07-02T14:08:23Z
|
2024-07-02T14:08:23Z
|
2407.02275
|
Learning Paradigms and Modelling Methodologies for Digital Twins in
Process Industry
|
Central to the digital transformation of the process industry are Digital Twins (DTs), virtual replicas of physical manufacturing systems that combine sensor data with sophisticated data-based or physics-based models, or a combination thereof, to tackle a variety of industrial-relevant tasks like process monitoring, predictive control or decision support. The backbone of a DT, i.e. the concrete modelling methodologies and architectural frameworks supporting these models, are complex, diverse and evolve fast, necessitating a thorough understanding of the latest state-of-the-art methods and trends to stay on top of a highly competitive market. From a research perspective, despite the high research interest in reviewing various aspects of DTs, structured literature reports specifically focusing on unravelling the utilized learning paradigms (e.g. self-supervised learning) for DT-creation in the process industry are a novel contribution in this field. This study aims to address these gaps by (1) systematically analyzing the modelling methodologies (e.g. Convolutional Neural Network, Encoder-Decoder, Hidden Markov Model) and paradigms (e.g. data-driven, physics-based, hybrid) used for DT-creation; (2) assessing the utilized learning strategies (e.g. supervised, unsupervised, self-supervised); (3) analyzing the type of modelling task (e.g. regression, classification, clustering); and (4) identifying the challenges and research gaps, as well as, discuss potential resolutions provided.
|
http://arxiv.org/pdf/2407.02275v1
|
[
"Michael Mayr",
"Georgios C. Chasparis",
"Josef Küng"
] |
2024-07-02T14:05:10Z
|
2024-07-02T14:05:10Z
|
2407.02271
|
Improving Explainability of Softmax Classifiers Using a Prototype-Based
Joint Embedding Method
|
We propose a prototype-based approach for improving explainability of softmax classifiers that provides an understandable prediction confidence, generated through stochastic sampling of prototypes, and demonstrates potential for out of distribution detection (OOD). By modifying the model architecture and training to make predictions using similarities to any set of class examples from the training dataset, we acquire the ability to sample for prototypical examples that contributed to the prediction, which provide an instance-based explanation for the model's decision. Furthermore, by learning relationships between images from the training dataset through relative distances within the model's latent space, we obtain a metric for uncertainty that is better able to detect out of distribution data than softmax confidence.
|
http://arxiv.org/pdf/2407.02271v1
|
[
"Hilarie Sit",
"Brendan Keith",
"Karianne Bergen"
] |
2024-07-02T13:59:09Z
|
2024-07-02T13:59:09Z
|
2407.02269
|
IFTT-PIN: A Self-Calibrating PIN-Entry Method
|
Personalising an interface to the needs and preferences of a user often incurs additional interaction steps. In this paper, we demonstrate a novel method that enables the personalising of an interface without the need for explicit calibration procedures, via a process we call self-calibration. A second-order effect of self-calibration is that an outside observer cannot easily infer what a user is trying to achieve because they cannot interpret the user's actions. To explore this security angle, we developed IFTT-PIN (If This Then PIN) as the first self-calibrating PIN-entry method. When using IFTT-PIN, users are free to choose any button for any meaning without ever explicitly communicating their choice to the machine. IFTT-PIN infers both the user's PIN and their preferred button mapping at the same time. This paper presents the concept, implementation, and interactive demonstrations of IFTT-PIN, as well as an evaluation against shoulder surfing attacks. Our study (N=24) shows that by adding self-calibration to an existing PIN entry method, IFTT-PIN statistically significantly decreased PIN attack decoding rate by ca. 8.5 times (p=1.1e-9), while only decreasing the PIN entry encoding rate by ca. 1.4 times (p=0.02), leading to a positive security-usability trade-off. IFTT-PIN's entry rate significantly improved 21 days after first exposure (p=3.6e-6) to the method, suggesting self-calibrating interfaces are memorable despite using an initially undefined user interface. Self-calibration methods might lead to novel opportunities for interaction that are more inclusive and versatile, a potentially interesting challenge for the community. A short introductory video is available at https://youtu.be/pP5sfniNRns.
|
http://arxiv.org/pdf/2407.02269v1
|
[
"Kathryn McConkey",
"Talha Enes Ayranci",
"Mohamed Khamis",
"Jonathan Grizou"
] |
2024-07-02T13:58:28Z
|
2024-07-02T13:58:28Z
|
2407.02265
|
DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing
|
Bringing a novel drug from the original idea to market typically requires more than ten years and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the approved drug to treat new diseases. The process is also known as drug repurposing or drug repositioning. Machine learning methods exhibited huge potential in automating drug repurposing. However, it still encounter some challenges, such as lack of labels and multimodal feature representation. To address these issues, we design DrugCLIP, a cutting-edge contrastive learning method, to learn drug and disease's interaction without negative labels. Additionally, we have curated a drug repurposing dataset based on real-world clinical trial records. Thorough empirical studies are conducted to validate the effectiveness of the proposed DrugCLIP method.
|
http://arxiv.org/pdf/2407.02265v1
|
[
"Yingzhou Lu",
"Yaojun Hu",
"Chenhao Li"
] |
2024-07-02T13:41:59Z
|
2024-07-02T13:41:59Z
|
2110.12484
|
Enabling Large Batch Size Training for DNN Models Beyond the Memory
Limit While Maintaining Performance
|
Recent deep learning models are difficult to train using a large batch size, because commodity machines may not have enough memory to accommodate both the model and a large data batch size. The batch size is one of the hyper-parameters used in the training model, and it is dependent on and is limited by the target machine memory capacity because the batch size can only fit into the remaining memory after the model is uploaded. Moreover, the data item size is also an important factor because if each data item size is larger then the batch size that can fit into the remaining memory becomes smaller. This paper proposes a method called Micro-Batch Processing (MBP) to address this problem. This method helps deep learning models to train by providing a batch processing method that splits a batch into a size that can fit in the remaining memory and processes them sequentially. After processing the small batches individually, a loss normalization algorithm based on the gradient accumulation is used to maintain the performance. The purpose of our method is to allow deep learning models to train using larger batch sizes that exceed the memory capacity of a system without increasing the memory size or using multiple devices (GPUs).
|
http://arxiv.org/abs/2110.12484v3
|
[
"XinYu Piao",
"DoangJoo Synn",
"JooYoung Park",
"Jong-Kook Kim"
] |
2024-07-02T13:33:39Z
|
2021-10-24T16:38:05Z
|
2405.03961
|
Structure-based drug design by denoising voxel grids
|
We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures. Our approach represents molecules as 3D atomic density grids and leverages a 3D voxel-denoising network for learning and generation. We extend the neural empirical Bayes formalism (Saremi & Hyvarinen, 2019) to the conditional setting and generate structure-conditioned molecules with a two-step procedure: (i) sample noisy molecules from the Gaussian-smoothed conditional distribution with underdamped Langevin MCMC using the learned score function and (ii) estimate clean molecules from the noisy samples with single-step denoising. Compared to the current state of the art, our model is simpler to train, significantly faster to sample from, and achieves better results on extensive in silico benchmarks -- the generated molecules are more diverse, exhibit fewer steric clashes, and bind with higher affinity to protein pockets. The code is available at https://github.com/genentech/voxbind/.
|
http://arxiv.org/pdf/2405.03961v2
|
[
"Pedro O. Pinheiro",
"Arian Jamasb",
"Omar Mahmood",
"Vishnu Sresht",
"Saeed Saremi"
] |
2024-07-02T13:28:28Z
|
2024-05-07T02:48:15Z
|
2407.02258
|
SiamTST: A Novel Representation Learning Framework for Enhanced
Multivariate Time Series Forecasting applied to Telco Networks
|
We introduce SiamTST, a novel representation learning framework for multivariate time series. SiamTST integrates a Siamese network with attention, channel-independent patching, and normalization techniques to achieve superior performance. Evaluated on a real-world industrial telecommunication dataset, SiamTST demonstrates significant improvements in forecasting accuracy over existing methods. Notably, a simple linear network also shows competitive performance, achieving the second-best results, just behind SiamTST. The code is available at https://github.com/simenkristoff/SiamTST.
|
http://arxiv.org/pdf/2407.02258v1
|
[
"Simen Kristoffersen",
"Peter Skaar Nordby",
"Sara Malacarne",
"Massimiliano Ruocco",
"Pablo Ortiz"
] |
2024-07-02T13:26:16Z
|
2024-07-02T13:26:16Z
|
2404.17701
|
Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning
in Particle Detector Readout
|
Embedded field programmable gate array (eFPGA) technology allows the implementation of reconfigurable logic within the design of an application-specific integrated circuit (ASIC). This approach offers the low power and efficiency of an ASIC along with the ease of FPGA configuration, particularly beneficial for the use case of machine learning in the data pipeline of next-generation collider experiments. An open-source framework called "FABulous" was used to design eFPGAs using 130 nm and 28 nm CMOS technology nodes, which were subsequently fabricated and verified through testing. The capability of an eFPGA to act as a front-end readout chip was assessed using simulation of high energy particles passing through a silicon pixel sensor. A machine learning-based classifier, designed for reduction of sensor data at the source, was synthesized and configured onto the eFPGA. A successful proof-of-concept was demonstrated through reproduction of the expected algorithm result on the eFPGA with perfect accuracy. Further development of the eFPGA technology and its application to collider detector readout is discussed.
|
http://arxiv.org/pdf/2404.17701v3
|
[
"Julia Gonski",
"Aseem Gupta",
"Haoyi Jia",
"Hyunjoon Kim",
"Lorenzo Rota",
"Larry Ruckman",
"Angelo Dragone",
"Ryan Herbst"
] |
2024-07-02T13:25:00Z
|
2024-04-26T20:59:23Z
|
2407.02253
|
Parameter-Selective Continual Test-Time Adaptation
|
Continual Test-Time Adaptation (CTTA) aims to adapt a pretrained model to ever-changing environments during the test time under continuous domain shifts. Most existing CTTA approaches are based on the Mean Teacher (MT) structure, which contains a student and a teacher model, where the student is updated using the pseudo-labels from the teacher model, and the teacher is then updated by exponential moving average strategy. However, these methods update the MT model indiscriminately on all parameters of the model. That is, some critical parameters involving sharing knowledge across different domains may be erased, intensifying error accumulation and catastrophic forgetting. In this paper, we introduce Parameter-Selective Mean Teacher (PSMT) method, which is capable of effectively updating the critical parameters within the MT network under domain shifts. First, we introduce a selective distillation mechanism in the student model, which utilizes past knowledge to regularize novel knowledge, thereby mitigating the impact of error accumulation. Second, to avoid catastrophic forgetting, in the teacher model, we create a mask through Fisher information to selectively update parameters via exponential moving average, with preservation measures applied to crucial parameters. Extensive experimental results verify that PSMT outperforms state-of-the-art methods across multiple benchmark datasets. Our code is available at url{https://github.com/JiaxuTian/PSMT}.
|
http://arxiv.org/pdf/2407.02253v1
|
[
"Jiaxu Tian",
"Fan Lyu"
] |
2024-07-02T13:18:15Z
|
2024-07-02T13:18:15Z
|
2407.02547
|
Domain Generalizable Knowledge Tracing via Concept Aggregation and
Relation-Based Attention
|
Knowledge Tracing (KT) is a critical task in online education systems, aiming to monitor students' knowledge states throughout a learning period. Common KT approaches involve predicting the probability of a student correctly answering the next question based on their exercise history. However, these methods often suffer from performance degradation when faced with the scarcity of student interactions in new education systems. To address this, we leverage student interactions from existing education systems to mitigate performance degradation caused by limited training data. Nevertheless, these interactions exhibit significant differences since they are derived from different education systems. To address this issue, we propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains, and new education systems with limited data are considered target domains. Additionally, we design a domain-generalizable knowledge tracing framework (DGKT) that can be applied to any KT model. Specifically, we present a concept aggregation approach designed to reduce conceptual disparities within sequences of student interactions from diverse domains. To further mitigate domain discrepancies, we introduce a novel normalization module called Sequence Instance Normalization (SeqIN). Moreover, to fully leverage exercise information, we propose a new knowledge tracing model tailored for the domain generalization KT task, named Domain-Generalizable Relation-based Knowledge Tracing (DGRKT). Extensive experiments across five benchmark datasets demonstrate that the proposed method performs well despite limited training data.
|
http://arxiv.org/pdf/2407.02547v1
|
[
"Yuquan Xie",
"Wanqi Yang",
"Jinyu Wei",
"Ming Yang",
"Yang Gao"
] |
2024-07-02T13:13:44Z
|
2024-07-02T13:13:44Z
|
2407.02546
|
Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors
|
In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.
|
http://arxiv.org/pdf/2407.02546v1
|
[
"Dinesh Cyril Selvaraj",
"Christian Vitale",
"Tania Panayiotou",
"Panayiotis Kolios",
"Carla Fabiana Chiasserini",
"Georgios Ellinas"
] |
2024-07-02T13:08:01Z
|
2024-07-02T13:08:01Z
|
2303.13093
|
Type-II Saddles and Probabilistic Stability of Stochastic Gradient
Descent
|
Characterizing and understanding the dynamics of stochastic gradient descent (SGD) around saddle points remains an open problem. We first show that saddle points in neural networks can be divided into two types, among which the Type-II saddles are especially difficult to escape from because the gradient noise vanishes at the saddle. The dynamics of SGD around these saddles are thus to leading order described by a random matrix product process, and it is thus natural to study the dynamics of SGD around these saddles using the notion of probabilistic stability and the related Lyapunov exponent. Theoretically, we link the study of SGD dynamics to well-known concepts in ergodic theory, which we leverage to show that saddle points can be either attractive or repulsive for SGD, and its dynamics can be classified into four different phases, depending on the signal-to-noise ratio in the gradient close to the saddle.
|
http://arxiv.org/pdf/2303.13093v4
|
[
"Liu Ziyin",
"Botao Li",
"Tomer Galanti",
"Masahito Ueda"
] |
2024-07-02T13:05:59Z
|
2023-03-23T08:17:10Z
|
2407.02240
|
MALT Powers Up Adversarial Attacks
|
Current adversarial attacks for multi-class classifiers choose the target class for a given input naively, based on the classifier's confidence levels for various target classes. We present a novel adversarial targeting method, textit{MALT - Mesoscopic Almost Linearity Targeting}, based on medium-scale almost linearity assumptions. Our attack wins over the current state of the art AutoAttack on the standard benchmark datasets CIFAR-100 and ImageNet and for a variety of robust models. In particular, our attack is emph{five times faster} than AutoAttack, while successfully matching all of AutoAttack's successes and attacking additional samples that were previously out of reach. We then prove formally and demonstrate empirically that our targeting method, although inspired by linear predictors, also applies to standard non-linear models.
|
http://arxiv.org/pdf/2407.02240v1
|
[
"Odelia Melamed",
"Gilad Yehudai",
"Adi Shamir"
] |
2024-07-02T13:02:12Z
|
2024-07-02T13:02:12Z
|
2407.02238
|
MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance
Optimizations
|
One of the primary areas of interest in High Performance Computing is the improvement of performance of parallel workloads. Nowadays, compilable source code-based optimization tasks that employ deep learning often exploit LLVM Intermediate Representations (IRs) for extracting features from source code. Most such works target specific tasks, or are designed with a pre-defined set of heuristics. So far, pre-trained models are rare in this domain, but the possibilities have been widely discussed. Especially approaches mimicking large-language models (LLMs) have been proposed. But these have prohibitively large training costs. In this paper, we propose MIREncoder, a M}ulti-modal IR-based Auto-Encoder that can be pre-trained to generate a learned embedding space to be used for downstream tasks by machine learning-based approaches. A multi-modal approach enables us to better extract features from compilable programs. It allows us to better model code syntax, semantics and structure. For code-based performance optimizations, these features are very important while making optimization decisions. A pre-trained model/embedding implicitly enables the usage of transfer learning, and helps move away from task-specific trained models. Additionally, a pre-trained model used for downstream performance optimization should itself have reduced overhead, and be easily usable. These considerations have led us to propose a modeling approach that i) understands code semantics and structure, ii) enables use of transfer learning, and iii) is small and simple enough to be easily re-purposed or reused even with low resource availability. Our evaluations will show that our proposed approach can outperform the state of the art while reducing overhead.
|
http://arxiv.org/pdf/2407.02238v1
|
[
"Akash Dutta",
"Ali Jannesari"
] |
2024-07-02T13:00:19Z
|
2024-07-02T13:00:19Z
|
2407.02233
|
Synthetic Multimodal Question Generation
|
Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. A key challenge with evaluating MMRAG is the paucity of high-quality datasets matching the question styles and modalities of interest. In light of this, we propose SMMQG, a synthetic data generation framework. SMMQG leverages interplay between a retriever, large language model (LLM) and large multimodal model (LMM) to generate question and answer pairs directly from multimodal documents, with the questions conforming to specified styles and modalities. We use SMMQG to generate an MMRAG dataset of 1024 questions over Wikipedia documents and evaluate state-of-the-art models using it, revealing insights into model performance that are attainable only through style- and modality-specific evaluation data. Next, we measure the quality of data produced by SMMQG via a human study. We find that the quality of our synthetic data is on par with the quality of the crowdsourced benchmark MMQA and that downstream evaluation results using both datasets strongly concur.
|
http://arxiv.org/pdf/2407.02233v1
|
[
"Ian Wu",
"Sravan Jayanthi",
"Vijay Viswanathan",
"Simon Rosenberg",
"Sina Pakazad",
"Tongshuang Wu",
"Graham Neubig"
] |
2024-07-02T12:57:42Z
|
2024-07-02T12:57:42Z
|
2407.02231
|
Safety-Driven Deep Reinforcement Learning Framework for Cobots: A
Sim2Real Approach
|
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrates specific parts of the safety requirements, such as velocity constraints, as specified by ISO 10218, directly within the DRL model that becomes a part of the robot's learning algorithm. The study then evaluated the efficiency of these safety constraints by subjecting the DRL model to various scenarios, including grasping tasks with and without obstacle avoidance. The validation process involved comprehensive simulation-based testing of the DRL model's responses to potential hazards and its compliance. Also, the performance of the system is carried out by the functional safety standards IEC 61508 to determine the safety integrity level. The study indicated a significant improvement in the safety performance of the robotic system. The proposed DRL model anticipates and mitigates hazards while maintaining operational efficiency. This study was validated in a testbed with a collaborative robotic arm with safety sensors and assessed with metrics such as the average number of safety violations, obstacle avoidance, and the number of successful grasps. The proposed approach outperforms the conventional method by a 16.5% average success rate on the tested scenarios in the simulations and 2.5% in the testbed without safety violations. The project repository is available at https://github.com/ammar-n-abbas/sim2real-ur-gym-gazebo.
|
http://arxiv.org/pdf/2407.02231v1
|
[
"Ammar N. Abbas",
"Shakra Mehak",
"Georgios C. Chasparis",
"John D. Kelleher",
"Michael Guilfoyle",
"Maria Chiara Leva",
"Aswin K Ramasubramanian"
] |
2024-07-02T12:56:17Z
|
2024-07-02T12:56:17Z
|
2405.17666
|
Structured Partial Stochasticity in Bayesian Neural Networks
|
Bayesian neural network posterior distributions have a great number of modes that correspond to the same network function. The abundance of such modes can make it difficult for approximate inference methods to do their job. Recent work has demonstrated the benefits of partial stochasticity for approximate inference in Bayesian neural networks; inference can be less costly and performance can sometimes be improved. I propose a structured way to select the deterministic subset of weights that removes neuron permutation symmetries, and therefore the corresponding redundant posterior modes. With a drastically simplified posterior distribution, the performance of existing approximate inference schemes is found to be greatly improved.
|
http://arxiv.org/pdf/2405.17666v2
|
[
"Tommy Rochussen"
] |
2024-07-02T12:55:33Z
|
2024-05-27T21:40:31Z
|
2406.04303
|
Vision-LSTM: xLSTM as Generic Vision Backbone
|
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to a scalable and performant architecture - the xLSTM - which overcomes long-standing LSTM limitations via exponential gating and parallelizable matrix memory structure. In this report, we introduce Vision-LSTM (ViL), an adaption of the xLSTM building blocks to computer vision. ViL comprises a stack of xLSTM blocks where odd blocks process the sequence of patch tokens from top to bottom while even blocks go from bottom to top. Experiments show that ViL holds promise to be further deployed as new generic backbone for computer vision architectures.
|
http://arxiv.org/pdf/2406.04303v2
|
[
"Benedikt Alkin",
"Maximilian Beck",
"Korbinian Pöppel",
"Sepp Hochreiter",
"Johannes Brandstetter"
] |
2024-07-02T12:39:46Z
|
2024-06-06T17:49:21Z
|
2308.01118
|
A Survey on Popularity Bias in Recommender Systems
|
Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations todays recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey, therefore, includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss todays literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.
|
http://arxiv.org/abs/2308.01118v3
|
[
"Anastasiia Klimashevskaia",
"Dietmar Jannach",
"Mehdi Elahi",
"Christoph Trattner"
] |
2024-07-02T12:39:41Z
|
2023-08-02T12:58:11Z
|
2407.02217
|
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style
Reinforcement Learning
|
Applying reinforcement learning (RL) to real-world applications requires addressing a trade-off between asymptotic performance, sample efficiency, and inference time. In this work, we demonstrate how to address this triple challenge by leveraging partial physical knowledge about the system dynamics. Our approach involves learning a physics-informed model to boost sample efficiency and generating imaginary trajectories from this model to learn a model-free policy and Q-function. Furthermore, we propose a hybrid planning strategy, combining the learned policy and Q-function with the learned model to enhance time efficiency in planning. Through practical demonstrations, we illustrate that our method improves the compromise between sample efficiency, time efficiency, and performance over state-of-the-art methods.
|
http://arxiv.org/pdf/2407.02217v1
|
[
"Zakariae El Asri",
"Olivier Sigaud",
"Nicolas Thome"
] |
2024-07-02T12:32:57Z
|
2024-07-02T12:32:57Z
|
2407.02211
|
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt
during Large Language Model Fine-tuning
|
Large language models (LLMs) have played a fundamental role in various natural language processing tasks with powerful prompt techniques. However, in real-world applications, there are often similar prompt components for repeated queries, which causes significant computational burdens during inference. Existing prompt compression and direct fine-tuning methods aim to tackle these challenges, yet they frequently struggle to strike an optimal balance between cost-efficiency and performance effectiveness, especially in complex tasks such as NL2Code. In this paper, we propose a novel method namely PromptIntern to internalize the prompt knowledge into model parameters via progressive fine-tuning. Our method enables LLMs to emulate the human learning process for a new task, where detailed templates and examples in a prompt are gradually internalized and phased out progressively as the model grows accustomed to the task. Extensive experiments demonstrate that our method reduces inference tokens over 90%, speedups inference by 4.2 times, and saves 88.3% monetary cost.
|
http://arxiv.org/pdf/2407.02211v1
|
[
"Jiaru Zou",
"Mengyu Zhou",
"Tao Li",
"Shi Han",
"Dongmei Zhang"
] |
2024-07-02T12:21:14Z
|
2024-07-02T12:21:14Z
|
2406.15897
|
Fusing Audio and Metadata Embeddings Improves Language-based Audio
Retrieval
|
Matching raw audio signals with textual descriptions requires understanding the audio's content and the description's semantics and then drawing connections between the two modalities. This paper investigates a hybrid retrieval system that utilizes audio metadata as an additional clue to understand the content of audio signals before matching them with textual queries. We experimented with metadata often attached to audio recordings, such as keywords and natural-language descriptions, and we investigated late and mid-level fusion strategies to merge audio and metadata. Our hybrid approach with keyword metadata and late fusion improved the retrieval performance over a content-based baseline by 2.36 and 3.69 pp. mAP@10 on the ClothoV2 and AudioCaps benchmarks, respectively.
|
http://arxiv.org/pdf/2406.15897v2
|
[
"Paul Primus",
"Gerhard Widmer"
] |
2024-07-02T12:13:14Z
|
2024-06-22T17:19:51Z
|
2305.13865
|
Selective Pre-training for Private Fine-tuning
|
Text prediction models, when used in applications like email clients or word processors, must protect user data privacy and adhere to model size constraints. These constraints are crucial to meet memory and inference time requirements, as well as to reduce inference costs. Building small, fast, and private domain-specific language models is a thriving area of research. In this work, we show that a careful pre-training on a emph{subset} of the public dataset that is guided by the private dataset is crucial to train small language models with differential privacy. On standard benchmarks, small models trained with our new framework achieve state-of-the-art performance. In addition to performance improvements, our results demonstrate that smaller models, through careful pre-training and private fine-tuning, can match the performance of much larger models that do not have access to private data. This underscores the potential of private learning for model compression and enhanced efficiency.
|
http://arxiv.org/pdf/2305.13865v3
|
[
"Da Yu",
"Sivakanth Gopi",
"Janardhan Kulkarni",
"Zinan Lin",
"Saurabh Naik",
"Tomasz Lukasz Religa",
"Jian Yin",
"Huishuai Zhang"
] |
2024-07-02T12:05:36Z
|
2023-05-23T09:36:58Z
|
2407.02191
|
Attack-Aware Noise Calibration for Differential Privacy
|
Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to limit the risk of information leakage. The scale of the added noise is critical, as it determines the trade-off between privacy and utility. The standard practice is to select the noise scale in terms of a privacy budget parameter $epsilon$. This parameter is in turn interpreted in terms of operational attack risk, such as accuracy, or sensitivity and specificity of inference attacks against the privacy of the data. We demonstrate that this two-step procedure of first calibrating the noise scale to a privacy budget $epsilon$, and then translating $epsilon$ to attack risk leads to overly conservative risk assessments and unnecessarily low utility. We propose methods to directly calibrate the noise scale to a desired attack risk level, bypassing the intermediate step of choosing $epsilon$. For a target attack risk, our approach significantly decreases noise scale, leading to increased utility at the same level of privacy. We empirically demonstrate that calibrating noise to attack sensitivity/specificity, rather than $epsilon$, when training privacy-preserving ML models substantially improves model accuracy for the same risk level. Our work provides a principled and practical way to improve the utility of privacy-preserving ML without compromising on privacy.
|
http://arxiv.org/pdf/2407.02191v1
|
[
"Bogdan Kulynych",
"Juan Felipe Gomez",
"Georgios Kaissis",
"Flavio du Pin Calmon",
"Carmela Troncoso"
] |
2024-07-02T11:49:59Z
|
2024-07-02T11:49:59Z
|
2407.02188
|
Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
|
Graph node classification with few labeled nodes presents significant challenges due to limited supervision. Conventional methods often exploit the graph in a transductive learning manner. They fail to effectively utilize the abundant unlabeled data and the structural information inherent in graphs. To address these issues, we introduce a Structure-Aware Consensus Network (SACN) from three perspectives. Firstly, SACN leverages a novel structure-aware consensus learning strategy between two strongly augmented views. The proposed strategy can fully exploit the potentially useful information of the unlabeled nodes and the structural information of the entire graph. Secondly, SACN uniquely integrates the graph's structural information to achieve strong-to-strong consensus learning, improving the utilization of unlabeled data while maintaining multiview learning. Thirdly, unlike two-branch graph neural network-based methods, SACN is designed for multiview feature learning within a single-branch architecture. Furthermore, a class-aware pseudolabel selection strategy helps address class imbalance and achieve effective weak-to-strong supervision. Extensive experiments on three benchmark datasets demonstrate SACN's superior performance in node classification tasks, particularly at very low label rates, outperforming state-of-the-art methods while maintaining computational simplicity.The source code is available at https://github.com/kunzhan/SACN
|
http://arxiv.org/pdf/2407.02188v1
|
[
"Shuaike Xu",
"Xiaolin Zhang",
"Peng Zhang",
"Kun Zhan"
] |
2024-07-02T11:46:07Z
|
2024-07-02T11:46:07Z
|
2406.14953
|
Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a
Novel Digital Biomarker for Cardiovascular Health
|
Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning. However, real-world age distributions are often imbalanced, posing significant challenges for deep learning models. In this paper, we introduce a novel, simple, and effective loss function named the Dist Loss to address deep imbalanced regression tasks. We trained a one-dimensional convolutional neural network (Net1D) incorporating the Dist Loss on the extensive UK Biobank dataset (n=502,389) to estimate vascular age from PPG signals and validate its efficacy in characterizing cardiovascular health. The model's performance was validated on a 40% held-out test set, achieving state-of-the-art results, especially in regions with small sample sizes. Furthermore, we divided the population into three subgroups based on the difference between predicted vascular age and chronological age: less than -10 years, between -10 and 10 years, and greater than 10 years. We analyzed the relationship between predicted vascular age and several cardiovascular events over a follow-up period of up to 10 years, including death, coronary heart disease, and heart failure. Our results indicate that the predicted vascular age has significant potential to reflect an individual's cardiovascular health status. Our code will be available at https://github.com/Ngk03/AI-vascular-age.
|
http://arxiv.org/pdf/2406.14953v2
|
[
"Guangkun Nie",
"Qinghao Zhao",
"Gongzheng Tang",
"Jun Li",
"Shenda Hong"
] |
2024-07-02T11:22:36Z
|
2024-06-21T08:04:12Z
|
2407.00063
|
An Interpretable Alternative to Neural Representation Learning for
Rating Prediction -- Transparent Latent Class Modeling of User Reviews
|
Nowadays, neural network (NN) and deep learning (DL) techniques are widely adopted in many applications, including recommender systems. Given the sparse and stochastic nature of collaborative filtering (CF) data, recent works have critically analyzed the effective improvement of neural-based approaches compared to simpler and often transparent algorithms for recommendation. Previous results showed that NN and DL models can be outperformed by traditional algorithms in many tasks. Moreover, given the largely black-box nature of neural-based methods, interpretable results are not naturally obtained. Following on this debate, we first present a transparent probabilistic model that topologically organizes user and product latent classes based on the review information. In contrast to popular neural techniques for representation learning, we readily obtain a statistical, visualization-friendly tool that can be easily inspected to understand user and product characteristics from a textual-based perspective. Then, given the limitations of common embedding techniques, we investigate the possibility of using the estimated interpretable quantities as model input for a rating prediction task. To contribute to the recent debates, we evaluate our results in terms of both capacity for interpretability and predictive performances in comparison with popular text-based neural approaches. The results demonstrate that the proposed latent class representations can yield competitive predictive performances, compared to popular, but difficult-to-interpret approaches.
|
http://arxiv.org/pdf/2407.00063v2
|
[
"Giuseppe Serra",
"Peter Tino",
"Zhao Xu",
"Xin Yao"
] |
2024-07-02T11:17:45Z
|
2024-06-17T07:07:42Z
|
2204.05192
|
Task-Synchronized Recurrent Neural Networks
|
Data are often sampled irregularly in time. Dealing with this using Recurrent Neural Networks (RNNs) traditionally involved ignoring the fact, feeding the time differences as additional inputs, or resampling the data. All these methods have their shortcomings. We propose an elegant straightforward alternative approach where instead the RNN is in effect resampled in time to match the time of the data or the task at hand. We use Echo State Network (ESN) and Gated Recurrent Unit (GRU) as the basis for our solution. Such RNNs can be seen as discretizations of continuous-time dynamical systems, which gives a solid theoretical ground to our approach. Our Task-Synchronized ESN (TSESN) and GRU (TSGRU) models allow for a direct model time setting and require no additional training, parameter tuning, or computation (solving differential equations or interpolating data) compared to their regular counterparts, thus retaining their original efficiency. We confirm empirically that our models can effectively compensate for the time-non-uniformity of the data and demonstrate that they compare favorably to data resampling, classical RNN methods, and alternative RNN models proposed to deal with time irregularities on several real-world nonuniform-time datasets. We open-source the code at https://github.com/oshapio/task-synchronized-RNNs .
|
http://arxiv.org/pdf/2204.05192v2
|
[
"Mantas Lukoševičius",
"Arnas Uselis"
] |
2024-07-02T11:15:19Z
|
2022-04-11T15:27:40Z
|
2407.00299
|
Human-Agent Joint Learning for Efficient Robot Manipulation Skill
Acquisition
|
Employing a teleoperation system for gathering demonstrations offers the potential for more efficient learning of robot manipulation. However, teleoperating a robot arm equipped with a dexterous hand or gripper, via a teleoperation system poses significant challenges due to its high dimensionality, complex motions, and differences in physiological structure. In this study, we introduce a novel system for joint learning between human operators and robots, that enables human operators to share control of a robot end-effector with a learned assistive agent, facilitating simultaneous human demonstration collection and robot manipulation teaching. In this setup, as data accumulates, the assistive agent gradually learns. Consequently, less human effort and attention are required, enhancing the efficiency of the data collection process. It also allows the human operator to adjust the control ratio to achieve a trade-off between manual and automated control. We conducted experiments in both simulated environments and physical real-world settings. Through user studies and quantitative evaluations, it is evident that the proposed system could enhance data collection efficiency and reduce the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks. Videos are available at https://norweig1an.github.io/human-agent-joint-learning.github.io/.
|
http://arxiv.org/pdf/2407.00299v2
|
[
"Shengcheng Luo",
"Quanquan Peng",
"Jun Lv",
"Kaiwen Hong",
"Katherine Rose Driggs-Campbell",
"Cewu Lu",
"Yong-Lu Li"
] |
2024-07-02T11:15:11Z
|
2024-06-29T03:37:29Z
|
2305.17043
|
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing
and Knowledge Discovery
|
Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the local (attributions per sample) and global (based on domain expert concepts) perspectives. We have established a set of sanity checks to identify sensible attribution methods, and we provide quantitative evidence in accordance with expert rules. This dataset-wide analysis goes beyond anecdotal evidence by aggregating data across patient subgroups. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.
|
http://arxiv.org/abs/2305.17043v2
|
[
"Patrick Wagner",
"Temesgen Mehari",
"Wilhelm Haverkamp",
"Nils Strodthoff"
] |
2024-07-02T10:58:23Z
|
2023-05-26T15:52:08Z
|
2407.02156
|
Towards Training Music Taggers on Synthetic Data
|
Most contemporary music tagging systems rely on large volumes of annotated data. As an alternative, we investigate the extent to which synthetically generated music excerpts can improve tagging systems when only small annotated collections are available. To this end, we release GTZAN-synth, a synthetic dataset that follows the taxonomy of the well-known GTZAN dataset while being ten times larger in data volume. We first observe that simply adding this synthetic dataset to the training split of GTZAN does not result into performance improvements. We then proceed to investigating domain adaptation, transfer learning and fine-tuning strategies for the task at hand and draw the conclusion that the last two options yield an increase in accuracy. Overall, the proposed approach can be considered as a first guide in a promising field for future research.
|
http://arxiv.org/pdf/2407.02156v1
|
[
"Nadine Kroher",
"Steven Manangu",
"Aggelos Pikrakis"
] |
2024-07-02T10:54:23Z
|
2024-07-02T10:54:23Z
|
2202.08536
|
Are There Exceptions to Goodhart's Law? On the Moral Justification of
Fairness-Aware Machine Learning
|
Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions designed to ensure that individuals who are affected by the predictions of a machine learning model are treated fairly. The problem is often posed as an optimization problem, where the objective is to achieve high predictive performance under a quantitative fairness constraint. However, any attempt to design a fair-ml algorithm must assume a world where Goodhart's law has an exception: when a fairness measure becomes an optimization constraint, it does not cease to be a good measure. In this paper, we argue that fairness measures are particularly sensitive to Goodhart's law. Our main contributions are as follows. First, we present a framework for moral reasoning about the justification of fairness metrics. In contrast to existing work, our framework incorporates the belief that whether a distribution of outcomes is fair, depends not only on the cause of inequalities but also on what moral claims decision subjects have to receive a particular benefit or avoid a burden. We use the framework to distil moral and empirical assumptions under which particular fairness metrics correspond to a fair distribution of outcomes. Second, we explore the extent to which employing fairness metrics as a constraint in a fair-ml algorithm is morally justifiable, exemplified by the fair-ml algorithm introduced by Hardt et al. (2016). We illustrate that enforcing a fairness metric through a fair-ml algorithm often does not result in the fair distribution of outcomes that motivated its use and can even harm the individuals the intervention was intended to protect.
|
http://arxiv.org/pdf/2202.08536v3
|
[
"Hilde Weerts",
"Lambèr Royakkers",
"Mykola Pechenizkiy"
] |
2024-07-02T10:53:59Z
|
2022-02-17T09:26:39Z
|
2407.02153
|
Equidistribution-based training of Free Knot Splines and ReLU Neural
Networks
|
We consider the problem of one-dimensional function approximation using shallow neural networks (NN) with a rectified linear unit (ReLU) activation function and compare their training with traditional methods such as univariate Free Knot Splines (FKS). ReLU NNs and FKS span the same function space, and thus have the same theoretical expressivity. In the case of ReLU NNs, we show that their ill-conditioning degrades rapidly as the width of the network increases. This often leads to significantly poorer approximation in contrast to the FKS representation, which remains well-conditioned as the number of knots increases. We leverage the theory of optimal piecewise linear interpolants to improve the training procedure for a ReLU NN. Using the equidistribution principle, we propose a two-level procedure for training the FKS by first solving the nonlinear problem of finding the optimal knot locations of the interpolating FKS. Determining the optimal knots then acts as a good starting point for training the weights of the FKS. The training of the FKS gives insights into how we can train a ReLU NN effectively to give an equally accurate approximation. More precisely, we combine the training of the ReLU NN with an equidistribution based loss to find the breakpoints of the ReLU functions, combined with preconditioning the ReLU NN approximation (to take an FKS form) to find the scalings of the ReLU functions, leads to a well-conditioned and reliable method of finding an accurate ReLU NN approximation to a target function. We test this method on a series or regular, singular, and rapidly varying target functions and obtain good results realising the expressivity of the network in this case.
|
http://arxiv.org/pdf/2407.02153v1
|
[
"Simone Appella",
"Simon Arridge",
"Chris Budd",
"Teo Deveney",
"Lisa Maria Kreusser"
] |
2024-07-02T10:51:36Z
|
2024-07-02T10:51:36Z
|
2407.02143
|
Counterfactual Data Augmentation with Denoising Diffusion for Graph
Anomaly Detection
|
A critical aspect of Graph Neural Networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the representations of abnormal nodes are prone to be averaged by normal neighbors, making the learned anomaly representations less distinguishable. To tackle this issue, we propose CAGAD -- an unsupervised Counterfactual data Augmentation method for Graph Anomaly Detection -- which introduces a graph pointer neural network as the heterophilic node detector to identify potential anomalies whose neighborhoods are normal-node-dominant. For each identified potential anomaly, we design a graph-specific diffusion model to translate a part of its neighbors, which are probably normal, into anomalous ones. At last, we involve these translated neighbors in GNN neighborhood aggregation to produce counterfactual representations of anomalies. Through aggregating the translated anomalous neighbors, counterfactual representations become more distinguishable and further advocate detection performance. The experimental results on four datasets demonstrate that CAGAD significantly outperforms strong baselines, with an average improvement of 2.35% on F1, 2.53% on AUC-ROC, and 2.79% on AUC-PR.
|
http://arxiv.org/pdf/2407.02143v1
|
[
"Chunjing Xiao",
"Shikang Pang",
"Xovee Xu",
"Xuan Li",
"Goce Trajcevski",
"Fan Zhou"
] |
2024-07-02T10:37:54Z
|
2024-07-02T10:37:54Z
|
2407.02138
|
Efficient Nearest Neighbor based Uncertainty Estimation for Natural
Language Processing Tasks
|
Trustworthy prediction in Deep Neural Networks (DNNs), including Pre-trained Language Models (PLMs) is important for safety-critical applications in the real world. However, DNNs often suffer from uncertainty estimation, such as miscalibration. In particular, approaches that require multiple stochastic inference can mitigate this problem, but the expensive cost of inference makes them impractical. In this study, we propose $k$-Nearest Neighbor Uncertainty Estimation ($k$NN-UE), which is an uncertainty estimation method that uses the distances from the neighbors and label-existence ratio of neighbors. Experiments on sentiment analysis, natural language inference, and named entity recognition show that our proposed method outperforms the baselines or recent density-based methods in confidence calibration, selective prediction, and out-of-distribution detection. Moreover, our analyses indicate that introducing dimension reduction or approximate nearest neighbor search inspired by recent $k$NN-LM studies reduces the inference overhead without significantly degrading estimation performance when combined them appropriately.
|
http://arxiv.org/pdf/2407.02138v1
|
[
"Wataru Hashimoto",
"Hidetaka Kamigaito",
"Taro Watanabe"
] |
2024-07-02T10:33:31Z
|
2024-07-02T10:33:31Z
|
2407.02125
|
Distributional Regression U-Nets for the Postprocessing of Precipitation
Ensemble Forecasts
|
Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming. We propose a global statistical postprocessing method for grid-based precipitation ensemble forecasts. This U-Net-based distributional regression method predicts marginal distributions in the form of parametric distributions inferred by scoring rule minimization. Distributional regression U-Nets are compared to state-of-the-art postprocessing methods for daily 21-h forecasts of 3-h accumulated precipitation over the South of France. Training data comes from the M'et'eo-France weather model AROME-EPS and spans 3 years. A practical challenge appears when consistent data or reforecasts are not available. Distributional regression U-Nets compete favorably with the raw ensemble. In terms of continuous ranked probability score, they reach a performance comparable to quantile regression forests (QRF). However, they are unable to provide calibrated forecasts in areas associated with high climatological precipitation. In terms of predictive power for heavy precipitation events, they outperform both QRF and semi-parametric QRF with tail extensions.
|
http://arxiv.org/pdf/2407.02125v1
|
[
"Romain Pic",
"Clément Dombry",
"Philippe Naveau",
"Maxime Taillardat"
] |
2024-07-02T10:16:04Z
|
2024-07-02T10:16:04Z
|
2401.10800
|
Estimation of AMOC transition probabilities using a machine learning
based rare-event algorithm
|
The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called ``committor function" is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function.
|
http://arxiv.org/pdf/2401.10800v3
|
[
"Valérian Jacques-Dumas",
"René M. van Westen",
"Henk A. Dijkstra"
] |
2024-07-02T10:06:43Z
|
2024-01-19T16:36:27Z
|
2407.02112
|
A Data-Centric Perspective on Evaluating Machine Learning Models for
Tabular Data
|
Tabular data is prevalent in real-world machine learning applications, and new models for supervised learning of tabular data are frequently proposed. Comparative studies assessing the performance of models typically consist of model-centric evaluation setups with overly standardized data preprocessing. This paper demonstrates that such model-centric evaluations are biased, as real-world modeling pipelines often require dataset-specific preprocessing and feature engineering. Therefore, we propose a data-centric evaluation framework. We select 10 relevant datasets from Kaggle competitions and implement expert-level preprocessing pipelines for each dataset. We conduct experiments with different preprocessing pipelines and hyperparameter optimization (HPO) regimes to quantify the impact of model selection, HPO, feature engineering, and test-time adaptation. Our main findings are: 1. After dataset-specific feature engineering, model rankings change considerably, performance differences decrease, and the importance of model selection reduces. 2. Recent models, despite their measurable progress, still significantly benefit from manual feature engineering. This holds true for both tree-based models and neural networks. 3. While tabular data is typically considered static, samples are often collected over time, and adapting to distribution shifts can be important even in supposedly static data. These insights suggest that research efforts should be directed toward a data-centric perspective, acknowledging that tabular data requires feature engineering and often exhibits temporal characteristics.
|
http://arxiv.org/pdf/2407.02112v1
|
[
"Andrej Tschalzev",
"Sascha Marton",
"Stefan Lüdtke",
"Christian Bartelt",
"Heiner Stuckenschmidt"
] |
2024-07-02T09:54:39Z
|
2024-07-02T09:54:39Z
|
2407.02106
|
Automated Knowledge Graph Learning in Industrial Processes
|
Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.
|
http://arxiv.org/pdf/2407.02106v1
|
[
"Lolitta Ammann",
"Jorge Martinez-Gil",
"Michael Mayr",
"Georgios C. Chasparis"
] |
2024-07-02T09:47:56Z
|
2024-07-02T09:47:56Z
|
2310.01210
|
Towards Robust Cardiac Segmentation using Graph Convolutional Networks
|
Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentation and can segment cardiac structures in real-time with average errors comparable to inter-observer variability. However, this architecture still generates large outliers that are often anatomically incorrect. This work uses the concept of graph convolutional neural networks that predict the contour points of the structures of interest instead of labeling each pixel. We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset. Additionally, this work contributes with an ablation study on the graph convolutional architecture and an evaluation of clinical measurements on the clinical HUNT4 dataset. Finally, we propose to use the inter-model agreement of the U-Net and the graph network as a predictor of both the input and segmentation quality. We show this predictor can detect out-of-distribution and unsuitable input images in real-time. Source code is available online: https://github.com/gillesvntnu/GCN_multistructure
|
http://arxiv.org/pdf/2310.01210v5
|
[
"Gilles Van De Vyver",
"Sarina Thomas",
"Guy Ben-Yosef",
"Sindre Hellum Olaisen",
"Håvard Dalen",
"Lasse Løvstakken",
"Erik Smistad"
] |
2024-07-02T09:31:04Z
|
2023-10-02T13:55:06Z
|
2407.02091
|
Efficient Bit Labeling in Factorization Machines with Annealing for
Traveling Salesman Problem
|
To efficiently find an optimum parameter combination in a large-scale problem, it is a key to convert the parameters into available variables in actual machines. Specifically, quadratic unconstrained binary optimization problems are solved with the help of machine learning, e.g., factorization machines with annealing, which convert a raw parameter to binary variables. This work investigates the dependence of the convergence speed and the accuracy on binary labeling method, which can influence the cost function shape and thus the probability of being captured at a local minimum solution. By exemplifying traveling salesman problem, we propose and evaluate Gray labeling, which correlates the Hamming distance in binary labels with the traveling distance. Through numerical simulation of traveling salesman problem up to 15 cities at a limited number of iterations, the Gray labeling shows less local minima percentages and shorter traveling distances compared with natural labeling.
|
http://arxiv.org/pdf/2407.02091v1
|
[
"Shota Koshikawa",
"Aruto Hosaka",
"Tsuyoshi Yoshida"
] |
2024-07-02T09:26:38Z
|
2024-07-02T09:26:38Z
|
2407.02089
|
GPTCast: a weather language model for precipitation nowcasting
|
This work introduces GPTCast, a generative deep-learning method for ensemble nowcast of radar-based precipitation, inspired by advancements in large language models (LLMs). We employ a GPT model as a forecaster to learn spatiotemporal precipitation dynamics using tokenized radar images. The tokenizer is based on a Quantized Variational Autoencoder featuring a novel reconstruction loss tailored for the skewed distribution of precipitation that promotes faithful reconstruction of high rainfall rates. The approach produces realistic ensemble forecasts and provides probabilistic outputs with accurate uncertainty estimation. The model is trained without resorting to randomness, all variability is learned solely from the data and exposed by model at inference for ensemble generation. We train and test GPTCast using a 6-year radar dataset over the Emilia-Romagna region in Northern Italy, showing superior results compared to state-of-the-art ensemble extrapolation methods.
|
http://arxiv.org/pdf/2407.02089v1
|
[
"Gabriele Franch",
"Elena Tomasi",
"Rishabh Wanjari",
"Virginia Poli",
"Chiara Cardinali",
"Pier Paolo Alberoni",
"Marco Cristoforetti"
] |
2024-07-02T09:25:58Z
|
2024-07-02T09:25:58Z
|
2406.12381
|
QOG:Question and Options Generation based on Language Model
|
Question-Options Generation (QOG) is a task that involves generating a set of question-options pairs given context. This task has various applications, including fine-tuning large models, information retrieval, and automated multiple-choice question generation for education. In this paper, we develop QOG models using three different methods based on fine-tuning sequence-to-sequence language models (LMs). Experiments demonstrate that the end-to-end QOG model is computationally efficient and stable during both training and inference, outperforming other methods. Furthermore, our analysis indicates that our QOG models are competitive on the QOG task compared to the large language model Llama 3-8B.
|
http://arxiv.org/pdf/2406.12381v2
|
[
"Jincheng Zhou"
] |
2024-07-02T09:21:03Z
|
2024-06-18T08:09:58Z
|
2312.00592
|
Tracking Object Positions in Reinforcement Learning: A Metric for
Keypoint Detection (extended version)
|
Reinforcement learning (RL) for robot control typically requires a detailed representation of the environment state, including information about task-relevant objects not directly measurable. Keypoint detectors, such as spatial autoencoders (SAEs), are a common approach to extracting a low-dimensional representation from high-dimensional image data. SAEs aim at spatial features such as object positions, which are often useful representations in robotic RL. However, whether an SAE is actually able to track objects in the scene and thus yields a spatial state representation well suited for RL tasks has rarely been examined due to a lack of established metrics. In this paper, we propose to assess the performance of an SAE instance by measuring how well keypoints track ground truth objects in images. We present a computationally lightweight metric and use it to evaluate common baseline SAE architectures on image data from a simulated robot task. We find that common SAEs differ substantially in their spatial extraction capability. Furthermore, we validate that SAEs that perform well in our metric achieve superior performance when used in downstream RL. Thus, our metric is an effective and lightweight indicator of RL performance before executing expensive RL training. Building on these insights, we identify three key modifications of SAE architectures to improve tracking performance.
|
http://arxiv.org/pdf/2312.00592v3
|
[
"Emma Cramer",
"Jonas Reiher",
"Sebastian Trimpe"
] |
2024-07-02T09:09:19Z
|
2023-12-01T13:56:28Z
|
2407.02073
|
Contribution Evaluation of Heterogeneous Participants in Federated
Learning via Prototypical Representations
|
Contribution evaluation in federated learning (FL) has become a pivotal research area due to its applicability across various domains, such as detecting low-quality datasets, enhancing model robustness, and designing incentive mechanisms. Existing contribution evaluation methods, which primarily rely on data volume, model similarity, and auxiliary test datasets, have shown success in diverse scenarios. However, their effectiveness often diminishes due to the heterogeneity of data distributions, presenting a significant challenge to their applicability. In response, this paper explores contribution evaluation in FL from an entirely new perspective of representation. In this work, we propose a new method for the contribution evaluation of heterogeneous participants in federated learning (FLCE), which introduces a novel indicator emph{class contribution momentum} to conduct refined contribution evaluation. Our core idea is the construction and application of the class contribution momentum indicator from individual, relative, and holistic perspectives, thereby achieving an effective and efficient contribution evaluation of heterogeneous participants without relying on an auxiliary test dataset. Extensive experimental results demonstrate the superiority of our method in terms of fidelity, effectiveness, efficiency, and heterogeneity across various scenarios.
|
http://arxiv.org/pdf/2407.02073v1
|
[
"Qi Guo",
"Minghao Yao",
"Zhen Tian",
"Saiyu Qi",
"Yong Qi",
"Yun Lin",
"Jin Song Dong"
] |
2024-07-02T09:05:43Z
|
2024-07-02T09:05:43Z
|
2405.07719
|
USP: A Unified Sequence Parallelism Approach for Long Context Generative
AI
|
Sequence parallelism (SP), which divides the sequence dimension of input tensors across multiple computational devices, is becoming key to unlocking the long-context capabilities of generative AI models. This paper investigates the state-of-the-art SP approaches, i.e. DeepSpeed-Ulysses and Ring-Attention, and proposes a unified SP approach, which is more robust to transformer model architectures and network hardware topology. This paper compares the communication and memory cost of SP and existing parallelism, including data/tensor/zero/pipeline parallelism, and discusses the best practices for designing hybrid 4D parallelism involving SP. We achieved 47% MFU on two 8xA800 nodes using SP for the LLAMA3-8B model training using sequence length 208K. Our code is publicly available at https://github.com/feifeibear/long-context-attention.
|
http://arxiv.org/pdf/2405.07719v5
|
[
"Jiarui Fang",
"Shangchun Zhao"
] |
2024-07-02T09:03:26Z
|
2024-05-13T13:08:02Z
|
2406.18382
|
Adversarial Search Engine Optimization for Large Language Models
|
Large Language Models (LLMs) are increasingly used in applications where the model selects from competing third-party content, such as in LLM-powered search engines or chatbot plugins. In this paper, we introduce Preference Manipulation Attacks, a new class of attacks that manipulate an LLM's selections to favor the attacker. We demonstrate that carefully crafted website content or plugin documentations can trick an LLM to promote the attacker products and discredit competitors, thereby increasing user traffic and monetization. We show this leads to a prisoner's dilemma, where all parties are incentivized to launch attacks, but the collective effect degrades the LLM's outputs for everyone. We demonstrate our attacks on production LLM search engines (Bing and Perplexity) and plugin APIs (for GPT-4 and Claude). As LLMs are increasingly used to rank third-party content, we expect Preference Manipulation Attacks to emerge as a significant threat.
|
http://arxiv.org/pdf/2406.18382v2
|
[
"Fredrik Nestaas",
"Edoardo Debenedetti",
"Florian Tramèr"
] |
2024-07-02T08:56:48Z
|
2024-06-26T14:24:51Z
|
2404.16958
|
A Closer Look at Classification Evaluation Metrics and a Critical
Reflection of Common Evaluation Practice
|
Classification systems are evaluated in a countless number of papers. However, we find that evaluation practice is often nebulous. Frequently, metrics are selected without arguments, and blurry terminology invites misconceptions. For instance, many works use so-called 'macro' metrics to rank systems (e.g., 'macro F1') but do not clearly specify what they would expect from such a `macro' metric. This is problematic, since picking a metric can affect research findings, and thus any clarity in the process should be maximized. Starting from the intuitive concepts of bias and prevalence, we perform an analysis of common evaluation metrics. The analysis helps us understand the metrics' underlying properties, and how they align with expectations as found expressed in papers. Then we reflect on the practical situation in the field, and survey evaluation practice in recent shared tasks. We find that metric selection is often not supported with convincing arguments, an issue that can make a system ranking seem arbitrary. Our work aims at providing overview and guidance for more informed and transparent metric selection, fostering meaningful evaluation.
|
http://arxiv.org/abs/2404.16958v2
|
[
"Juri Opitz"
] |
2024-07-02T08:53:09Z
|
2024-04-25T18:12:43Z
|
2106.15775
|
Koopman Spectrum Nonlinear Regulators and Efficient Online Learning
|
Most modern reinforcement learning algorithms optimize a cumulative single-step cost along a trajectory. The optimized motions are often 'unnatural', representing, for example, behaviors with sudden accelerations that waste energy and lack predictability. In this work, we present a novel paradigm of controlling nonlinear systems via the minimization of the Koopman spectrum cost: a cost over the Koopman operator of the controlled dynamics. This induces a broader class of dynamical behaviors that evolve over stable manifolds such as nonlinear oscillators, closed loops, and smooth movements. We demonstrate that some dynamics characterizations that are not possible with a cumulative cost are feasible in this paradigm, which generalizes the classical eigenstructure and pole assignments to nonlinear decision making. Moreover, we present a sample efficient online learning algorithm for our problem that enjoys a sub-linear regret bound under some structural assumptions.
|
http://arxiv.org/pdf/2106.15775v2
|
[
"Motoya Ohnishi",
"Isao Ishikawa",
"Kendall Lowrey",
"Masahiro Ikeda",
"Sham Kakade",
"Yoshinobu Kawahara"
] |
2024-07-02T08:53:08Z
|
2021-06-30T02:07:39Z
|
2407.02062
|
Are Data Augmentation Methods in Named Entity Recognition Applicable for
Uncertainty Estimation?
|
This work investigates the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. For the future advance of NER in safety-critical fields like healthcare and finance, it is essential to achieve accurate predictions with calibrated confidence when applying Deep Neural Networks (DNNs), including Pre-trained Language Models (PLMs), as a real-world application. However, DNNs are prone to miscalibration, which limits their applicability. Moreover, existing methods for calibration and uncertainty estimation are computational expensive. Our investigation in NER found that data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting. Furthermore, we showed that the calibration for NER tends to be more effective when the perplexity of the sentences generated by data augmentation is lower, and that increasing the size of the augmentation further improves calibration and uncertainty.
|
http://arxiv.org/pdf/2407.02062v1
|
[
"Wataru Hashimoto",
"Hidetaka Kamigaito",
"Taro Watanabe"
] |
2024-07-02T08:49:43Z
|
2024-07-02T08:49:43Z
|
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