categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.02298 | null | null | http://arxiv.org/pdf/2406.02298v1 | 2024-06-04T13:19:06Z | 2024-06-04T13:19:06Z | Solving Partial Differential Equations in Different Domains by Operator
Learning method Based on Boundary Integral Equations | This article explores operator learning models that can deduce solutions to partial differential equations (PDEs) on arbitrary domains without requiring retraining. We introduce two innovative models rooted in boundary integral equations (BIEs): the Boundary Integral Type Deep Operator Network (BI-DeepONet) and the Boundary Integral Trigonometric Deep Operator Neural Network (BI-TDONet), which are crafted to address PDEs across diverse domains. Once fully trained, these BIE-based models adeptly predict the solutions of PDEs in any domain without the need for additional training. BI-TDONet notably enhances its performance by employing the singular value decomposition (SVD) of bounded linear operators, allowing for the efficient distribution of input functions across its modules. Furthermore, to tackle the issue of function sampling values that do not effectively capture oscillatory and impulse signal characteristics, trigonometric coefficients are utilized as both inputs and outputs in BI-TDONet. Our numerical experiments robustly support and confirm the efficacy of this theoretical framework. | [
"['Bin Meng' 'Yutong Lu' 'Ying Jiang']"
] |
null | null | 2406.02300 | null | null | http://arxiv.org/pdf/2406.02300v1 | 2024-06-04T13:29:12Z | 2024-06-04T13:29:12Z | Node-Level Topological Representation Learning on Point Clouds | Topological Data Analysis (TDA) allows us to extract powerful topological and higher-order information on the global shape of a data set or point cloud. Tools like Persistent Homology or the Euler Transform give a single complex description of the global structure of the point cloud. However, common machine learning applications like classification require point-level information and features to be available. In this paper, we bridge this gap and propose a novel method to extract node-level topological features from complex point clouds using discrete variants of concepts from algebraic topology and differential geometry. We verify the effectiveness of these topological point features (TOPF) on both synthetic and real-world data and study their robustness under noise. | [
"['Vincent P. Grande' 'Michael T. Schaub']"
] |
null | null | 2406.02309 | null | null | http://arxiv.org/pdf/2406.02309v2 | 2024-06-05T12:10:16Z | 2024-06-04T13:41:00Z | Effects of Exponential Gaussian Distribution on (Double Sampling)
Randomized Smoothing | Randomized Smoothing (RS) is currently a scalable certified defense method providing robustness certification against adversarial examples. Although significant progress has been achieved in providing defenses against $ell_p$ adversaries, the interaction between the smoothing distribution and the robustness certification still remains vague. In this work, we comprehensively study the effect of two families of distributions, named Exponential Standard Gaussian (ESG) and Exponential General Gaussian (EGG) distributions, on Randomized Smoothing and Double Sampling Randomized Smoothing (DSRS). We derive an analytic formula for ESG's certified radius, which converges to the origin formula of RS as the dimension $d$ increases. Additionally, we prove that EGG can provide tighter constant factors than DSRS in providing $Omega(sqrt{d})$ lower bounds of $ell_2$ certified radius, and thus further addresses the curse of dimensionality in RS. Our experiments on real-world datasets confirm our theoretical analysis of the ESG distributions, that they provide almost the same certification under different exponents $eta$ for both RS and DSRS. In addition, EGG brings a significant improvement to the DSRS certification, but the mechanism can be different when the classifier properties are different. Compared to the primitive DSRS, the increase in certified accuracy provided by EGG is prominent, up to 6.4% on ImageNet. | [
"['Youwei Shu' 'Xi Xiao' 'Derui Wang' 'Yuxin Cao' 'Siji Chen' 'Jason Xue'\n 'Linyi Li' 'Bo Li']"
] |
null | null | 2406.02310 | null | null | http://arxiv.org/pdf/2406.02310v1 | 2024-06-04T13:41:07Z | 2024-06-04T13:41:07Z | Disentangled Representation via Variational AutoEncoder for Continuous
Treatment Effect Estimation | Continuous treatment effect estimation holds significant practical importance across various decision-making and assessment domains, such as healthcare and the military. However, current methods for estimating dose-response curves hinge on balancing the entire representation by treating all covariates as confounding variables. Although various approaches disentangle covariates into different factors for treatment effect estimation, they are confined to binary treatment settings. Moreover, observational data are often tainted with non-causal noise information that is imperceptible to the human. Hence, in this paper, we propose a novel Dose-Response curve estimator via Variational AutoEncoder (DRVAE) disentangled covariates representation. Our model is dedicated to disentangling covariates into instrumental factors, confounding factors, adjustment factors, and external noise factors, thereby facilitating the estimation of treatment effects under continuous treatment settings by balancing the disentangled confounding factors. Extensive results on synthetic and semi-synthetic datasets demonstrate that our model outperforms the current state-of-the-art methods. | [
"['Ruijing Cui' 'Jianbin Sun' 'Bingyu He' 'Kewei Yang' 'Bingfeng Ge']"
] |
null | null | 2406.02313 | null | null | http://arxiv.org/pdf/2406.02313v2 | 2024-06-12T16:08:29Z | 2024-06-04T13:42:42Z | Neural Thermodynamic Integration: Free Energies from Energy-based
Diffusion Models | Thermodynamic integration (TI) offers a rigorous method for estimating free-energy differences by integrating over a sequence of interpolating conformational ensembles. However, TI calculations are computationally expensive and typically limited to coupling a small number of degrees of freedom due to the need to sample numerous intermediate ensembles with sufficient conformational-space overlap. In this work, we propose to perform TI along an alchemical pathway represented by a trainable neural network, which we term Neural TI. Critically, we parametrize a time-dependent Hamiltonian interpolating between the interacting and non-interacting systems, and optimize its gradient using a denoising-diffusion objective. The ability of the resulting energy-based diffusion model to sample all intermediate ensembles allows us to perform TI from a single reference calculation. We apply our method to Lennard-Jones fluids, where we report accurate calculations of the excess chemical potential, demonstrating that Neural TI is capable of coupling hundreds of degrees of freedom at once. | [
"['Bálint Máté' 'François Fleuret' 'Tristan Bereau']"
] |
null | null | 2406.02315 | null | null | http://arxiv.org/pdf/2406.02315v2 | 2024-06-09T17:55:51Z | 2024-06-04T13:44:39Z | An Independence-promoting Loss for Music Generation with Language Models | Music generation schemes using language modeling rely on a vocabulary of audio tokens, generally provided as codes in a discrete latent space learnt by an auto-encoder. Multi-stage quantizers are often employed to produce these tokens, therefore the decoding strategy used for token prediction must be adapted to account for multiple codebooks: either it should model the joint distribution over all codebooks, or fit the product of the codebook marginal distributions. Modelling the joint distribution requires a costly increase in the number of auto-regressive steps, while fitting the product of the marginals yields an inexact model unless the codebooks are mutually independent. In this work, we introduce an independence-promoting loss to regularize the auto-encoder used as the tokenizer in language models for music generation. The proposed loss is a proxy for mutual information based on the maximum mean discrepancy principle, applied in reproducible kernel Hilbert spaces. Our criterion is simple to implement and train, and it is generalizable to other multi-stream codecs. We show that it reduces the statistical dependence between codebooks during auto-encoding. This leads to an increase in the generated music quality when modelling the product of the marginal distributions, while generating audio much faster than the joint distribution model. | [
"['Jean-Marie Lemercier' 'Simon Rouard' 'Jade Copet' 'Yossi Adi'\n 'Alexandre Défossez']"
] |
null | null | 2406.02317 | null | null | http://arxiv.org/pdf/2406.02317v1 | 2024-06-04T13:45:35Z | 2024-06-04T13:45:35Z | Generative Conditional Distributions by Neural (Entropic) Optimal
Transport | Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to multiple instances of the covariates. We introduce a novel neural entropic optimal transport method designed to effectively learn generative models of conditional distributions, particularly in scenarios characterized by limited sample sizes. Our method relies on the minimax training of two neural networks: a generative network parametrizing the inverse cumulative distribution functions of the conditional distributions and another network parametrizing the conditional Kantorovich potential. To prevent overfitting, we regularize the objective function by penalizing the Lipschitz constant of the network output. Our experiments on real-world datasets show the effectiveness of our algorithm compared to state-of-the-art conditional distribution learning techniques. Our implementation can be found at https://github.com/nguyenngocbaocmt02/GENTLE. | [
"['Bao Nguyen' 'Binh Nguyen' 'Hieu Trung Nguyen' 'Viet Anh Nguyen']"
] |
null | null | 2406.02318 | null | null | http://arxiv.org/pdf/2406.02318v2 | 2024-07-04T11:00:25Z | 2024-06-04T13:51:08Z | PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly
Detection | With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. In this setting, time series anomaly detection is practically important. It endeavors to identify deviant samples from the normal sample distribution in time series. Existing approaches generally assume that all the time series is available at a central location. However, we are witnessing the decentralized collection of time series due to the deployment of various edge devices. To bridge the gap between the decentralized time series data and the centralized anomaly detection algorithms, we propose a Parameter-efficient Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. PeFAD for the first time employs the pre-trained language model (PLM) as the body of the client's local model, which can benefit from its cross-modality knowledge transfer capability. To reduce the communication overhead and local model adaptation cost, we propose a parameter-efficient federated training module such that clients only need to fine-tune small-scale parameters and transmit them to the server for update. PeFAD utilizes a novel anomaly-driven mask selection strategy to mitigate the impact of neglected anomalies during training. A knowledge distillation operation on a synthetic privacy-preserving dataset that is shared by all the clients is also proposed to address the data heterogeneity issue across clients. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%. | [
"['Ronghui Xu' 'Hao Miao' 'Senzhang Wang' 'Philip S. Yu' 'Jianxin Wang']"
] |
null | null | 2406.02322 | null | null | http://arxiv.org/pdf/2406.02322v1 | 2024-06-04T13:52:42Z | 2024-06-04T13:52:42Z | A Survey of Transformer Enabled Time Series Synthesis | Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art. Application of these models to time series generation is less explored, however, and is of great utility to machine learning, privacy preservation, and explainability research. The present survey identifies this gap at the intersection of the transformer, generative AI, and time series data, and reviews works in this sparsely populated subdomain. The reviewed works show great variety in approach, and have not yet converged on a conclusive answer to the problems the domain poses. GANs, diffusion models, state space models, and autoencoders were all encountered alongside or surrounding the transformers which originally motivated the survey. While too open a domain to offer conclusive insights, the works surveyed are quite suggestive, and several recommendations for best practice, and suggestions of valuable future work, are provided. | [
"['Alexander Sommers' 'Logan Cummins' 'Sudip Mittal' 'Shahram Rahimi'\n 'Maria Seale' 'Joseph Jaboure' 'Thomas Arnold']"
] |
null | null | 2406.02327 | null | null | http://arxiv.org/pdf/2406.02327v1 | 2024-06-04T13:57:34Z | 2024-06-04T13:57:34Z | Continual Unsupervised Out-of-Distribution Detection | Deep learning models excel when the data distribution during training aligns with testing data. Yet, their performance diminishes when faced with out-of-distribution (OOD) samples, leading to great interest in the field of OOD detection. Current approaches typically assume that OOD samples originate from an unconcentrated distribution complementary to the training distribution. While this assumption is appropriate in the traditional unsupervised OOD (U-OOD) setting, it proves inadequate when considering the place of deployment of the underlying deep learning model. To better reflect this real-world scenario, we introduce the novel setting of continual U-OOD detection. To tackle this new setting, we propose a method that starts from a U-OOD detector, which is agnostic to the OOD distribution, and slowly updates during deployment to account for the actual OOD distribution. Our method uses a new U-OOD scoring function that combines the Mahalanobis distance with a nearest-neighbor approach. Furthermore, we design a confidence-scaled few-shot OOD detector that outperforms previous methods. We show our method greatly improves upon strong baselines from related fields. | [
"['Lars Doorenbos' 'Raphael Sznitman' 'Pablo Márquez-Neila']"
] |
null | null | 2406.02329 | null | null | http://arxiv.org/pdf/2406.02329v1 | 2024-06-04T13:58:28Z | 2024-06-04T13:58:28Z | On Affine Homotopy between Language Encoders | Pre-trained language encoders -- functions that represent text as vectors -- are an integral component of many NLP tasks. We tackle a natural question in language encoder analysis: What does it mean for two encoders to be similar? We contend that a faithful measure of similarity needs to be emph{intrinsic}, that is, task-independent, yet still be informative of emph{extrinsic} similarity -- the performance on downstream tasks. It is common to consider two encoders similar if they are emph{homotopic}, i.e., if they can be aligned through some transformation. In this spirit, we study the properties of emph{affine} alignment of language encoders and its implications on extrinsic similarity. We find that while affine alignment is fundamentally an asymmetric notion of similarity, it is still informative of extrinsic similarity. We confirm this on datasets of natural language representations. Beyond providing useful bounds on extrinsic similarity, affine intrinsic similarity also allows us to begin uncovering the structure of the space of pre-trained encoders by defining an order over them. | [
"['Robin SM Chan' 'Reda Boumasmoud' 'Anej Svete' 'Yuxin Ren' 'Qipeng Guo'\n 'Zhijing Jin' 'Shauli Ravfogel' 'Mrinmaya Sachan' 'Bernhard Schölkopf'\n 'Mennatallah El-Assady' 'Ryan Cotterell']"
] |
null | null | 2406.02332 | null | null | http://arxiv.org/pdf/2406.02332v1 | 2024-06-04T14:00:25Z | 2024-06-04T14:00:25Z | Extended Mind Transformers | Pre-trained language models demonstrate general intelligence and common sense, but long inputs quickly become a bottleneck for memorizing information at inference time. We resurface a simple method, Memorizing Transformers (Wu et al., 2022), that gives the model access to a bank of pre-computed memories. We show that it is possible to fix many of the shortcomings of the original method, such as the need for fine-tuning, by critically assessing how positional encodings should be updated for the keys and values retrieved. This intuitive method uses the model's own key/query system to select and attend to the most relevant memories at each generation step, rather than using external embeddings. We demonstrate the importance of external information being retrieved in a majority of decoder layers, contrary to previous work. We open source a new counterfactual long-range retrieval benchmark, and show that Extended Mind Transformers outperform today's state of the art by 6% on average. | [
"['Phoebe Klett' 'Thomas Ahle']"
] |
null | null | 2406.02333 | null | null | http://arxiv.org/pdf/2406.02333v1 | 2024-06-04T14:01:03Z | 2024-06-04T14:01:03Z | Towards Neural Architecture Search for Transfer Learning in 6G Networks | The future 6G network is envisioned to be AI-native, and as such, ML models will be pervasive in support of optimizing performance, reducing energy consumption, and in coping with increasing complexity and heterogeneity. A key challenge is automating the process of finding optimal model architectures satisfying stringent requirements stemming from varying tasks, dynamicity and available resources in the infrastructure and deployment positions. In this paper, we describe and review the state-of-the-art in Neural Architecture Search and Transfer Learning and their applicability in networking. Further, we identify open research challenges and set directions with a specific focus on three main requirements with elements unique to the future network, namely combining NAS and TL, multi-objective search, and tabular data. Finally, we outline and discuss both near-term and long-term work ahead. | [
"['Adam Orucu' 'Farnaz Moradi' 'Masoumeh Ebrahimi' 'Andreas Johnsson']"
] |
null | null | 2406.02336 | null | null | http://arxiv.org/pdf/2406.02336v1 | 2024-06-04T14:06:15Z | 2024-06-04T14:06:15Z | Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality
Constraints for Enhanced Function and PDE Approximation | We present polynomial-augmented neural networks (PANNs), a novel machine learning architecture that combines deep neural networks (DNNs) with a polynomial approximant. PANNs combine the strengths of DNNs (flexibility and efficiency in higher-dimensional approximation) with those of polynomial approximation (rapid convergence rates for smooth functions). To aid in both stable training and enhanced accuracy over a variety of problems, we present (1) a family of orthogonality constraints that impose mutual orthogonality between the polynomial and the DNN within a PANN; (2) a simple basis pruning approach to combat the curse of dimensionality introduced by the polynomial component; and (3) an adaptation of a polynomial preconditioning strategy to both DNNs and polynomials. We test the resulting architecture for its polynomial reproduction properties, ability to approximate both smooth functions and functions of limited smoothness, and as a method for the solution of partial differential equations (PDEs). Through these experiments, we demonstrate that PANNs offer superior approximation properties to DNNs for both regression and the numerical solution of PDEs, while also offering enhanced accuracy over both polynomial and DNN-based regression (each) when regressing functions with limited smoothness. | [
"['Madison Cooley' 'Shandian Zhe' 'Robert M. Kirby' 'Varun Shankar']"
] |
null | null | 2406.02343 | null | null | http://arxiv.org/pdf/2406.02343v2 | 2024-06-06T04:15:44Z | 2024-06-04T14:19:50Z | Cluster-Aware Similarity Diffusion for Instance Retrieval | Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph. However, existing techniques that construct the affinity graph based on pairwise instances can lead to the propagation of misinformation from outliers and other manifolds, resulting in inaccurate results. To overcome this issue, we propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval. The primary concept of CAS is to conduct similarity diffusion within local clusters, which can reduce the influence from other manifolds explicitly. To obtain a symmetrical and smooth similarity matrix, our Bidirectional Similarity Diffusion strategy introduces an inverse constraint term to the optimization objective of local cluster diffusion. Additionally, we have optimized a Neighbor-guided Similarity Smoothing approach to ensure similarity consistency among the local neighbors of each instance. Evaluations in instance retrieval and object re-identification validate the effectiveness of the proposed CAS, our code is publicly available. | [
"['Jifei Luo' 'Hantao Yao' 'Changsheng Xu']"
] |
null | null | 2406.02344 | null | null | http://arxiv.org/pdf/2406.02344v2 | 2024-06-05T10:23:41Z | 2024-06-04T14:20:10Z | Incorporating Navigation Context into Inland Vessel Trajectory
Prediction: A Gaussian Mixture Model and Transformer Approach | Using data sources beyond the Automatic Identification System to represent the context a vessel is navigating in and consequently improve situation awareness is still rare in machine learning approaches to vessel trajectory prediction (VTP). In inland shipping, where vessel movement is constrained within fairways, navigational context information is indispensable. In this contribution targeting inland VTP, Gaussian Mixture Models (GMMs) are applied, on a fused dataset of AIS and discharge measurements, to generate multi-modal distribution curves, capturing typical lateral vessel positioning in the fairway and dislocation speeds along the waterway. By sampling the probability density curves of the GMMs, feature vectors are derived which are used, together with spatio-temporal vessel features and fairway geometries, as input to a VTP transformer model. The incorporation of these distribution features of both the current and forthcoming navigation context improves prediction accuracy. The superiority of the model over a previously proposed transformer model for inland VTP is shown. The novelty lies in the provision of preprocessed, statistics-based features representing the conditioned spatial context, rather than relying on the model to extract relevant features for the VTP task from contextual data. Oversimplification of the complexity of inland navigation patterns by assuming a single typical route or selecting specific clusters prior to model application is avoided by giving the model access to the entire distribution information. The methodology's generalizability is demonstrated through the usage of data of 3 distinct river sections. It can be integrated into an interaction-aware prediction framework, where insights into the positioning of the actual vessel behavior in the overall distribution at the current location and discharge can enhance trajectory prediction accuracy. | [
"['Kathrin Donandt' 'Dirk Söffker']"
] |
null | null | 2406.02345 | null | null | http://arxiv.org/pdf/2406.02345v1 | 2024-06-04T14:21:41Z | 2024-06-04T14:21:41Z | Progressive Confident Masking Attention Network for Audio-Visual
Segmentation | Audio and visual signals typically occur simultaneously, and humans possess an innate ability to correlate and synchronize information from these two modalities. Recently, a challenging problem known as Audio-Visual Segmentation (AVS) has emerged, intending to produce segmentation maps for sounding objects within a scene. However, the methods proposed so far have not sufficiently integrated audio and visual information, and the computational costs have been extremely high. Additionally, the outputs of different stages have not been fully utilized. To facilitate this research, we introduce a novel Progressive Confident Masking Attention Network (PMCANet). It leverages attention mechanisms to uncover the intrinsic correlations between audio signals and visual frames. Furthermore, we design an efficient and effective cross-attention module to enhance semantic perception by selecting query tokens. This selection is determined through confidence-driven units based on the network's multi-stage predictive outputs. Experiments demonstrate that our network outperforms other AVS methods while requiring less computational resources. | [
"['Yuxuan Wang' 'Feng Dong' 'Jinchao Zhu']"
] |
null | null | 2406.02347 | null | null | http://arxiv.org/pdf/2406.02347v2 | 2024-06-05T21:21:33Z | 2024-06-04T14:23:27Z | Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few
Steps Image Generation | In this paper, we propose an efficient, fast, and versatile distillation method to accelerate the generation of pre-trained diffusion models: Flash Diffusion. The method reaches state-of-the-art performances in terms of FID and CLIP-Score for few steps image generation on the COCO2014 and COCO2017 datasets, while requiring only several GPU hours of training and fewer trainable parameters than existing methods. In addition to its efficiency, the versatility of the method is also exposed across several tasks such as text-to-image, inpainting, face-swapping, super-resolution and using different backbones such as UNet-based denoisers (SD1.5, SDXL) or DiT (Pixart-$alpha$), as well as adapters. In all cases, the method allowed to reduce drastically the number of sampling steps while maintaining very high-quality image generation. The official implementation is available at https://github.com/gojasper/flash-diffusion. | [
"['Clement Chadebec' 'Onur Tasar' 'Eyal Benaroche' 'Benjamin Aubin']"
] |
null | null | 2406.02348 | null | null | http://arxiv.org/pdf/2406.02348v1 | 2024-06-04T14:24:30Z | 2024-06-04T14:24:30Z | AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph
Neural Networks For Enhanced Unified Representation | While Multi-view Graph Neural Networks (MVGNNs) excel at leveraging diverse modalities for learning object representation, existing methods assume identical local topology structures across modalities that overlook real-world discrepancies. This leads MVGNNs straggles in modality fusion and representations denoising. To address these issues, we propose adaptive modality-wise structure learning (AMoSL). AMoSL captures node correspondences between modalities via optimal transport, and jointly learning with graph embedding. To enable efficient end-to-end training, we employ an efficient solution for the resulting complex bilevel optimization problem. Furthermore, AMoSL adapts to downstream tasks through unsupervised learning on inter-modality distances. The effectiveness of AMoSL is demonstrated by its ability to train more accurate graph classifiers on six benchmark datasets. | [
"['Peiyu Liang' 'Hongchang Gao' 'Xubin He']"
] |
null | null | 2406.02352 | null | null | http://arxiv.org/pdf/2406.02352v1 | 2024-06-04T14:28:36Z | 2024-06-04T14:28:36Z | System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization | We consider the problem of optimizing initial conditions and timing in dynamical systems governed by unknown ordinary differential equations (ODEs), where evaluating different initial conditions is costly and there are constraints on observation times. To identify the optimal conditions within several trials, we introduce a few-shot Bayesian Optimization (BO) framework based on the system's prior information. At the core of our approach is the System-Aware Neural ODE Processes (SANODEP), an extension of Neural ODE Processes (NODEP) designed to meta-learn ODE systems from multiple trajectories using a novel context embedding block. Additionally, we propose a multi-scenario loss function specifically for optimization purposes. Our two-stage BO framework effectively incorporates search space constraints, enabling efficient optimization of both initial conditions and observation timings. We conduct extensive experiments showcasing SANODEP's potential for few-shot BO. We also explore SANODEP's adaptability to varying levels of prior information, highlighting the trade-off between prior flexibility and model fitting accuracy. | [
"['Jixiang Qing' 'Becky D Langdon' 'Robert M Lee' 'Behrang Shafei'\n 'Mark van der Wilk' 'Calvin Tsay' 'Ruth Misener']"
] |
null | null | 2406.02354 | null | null | http://arxiv.org/pdf/2406.02354v1 | 2024-06-04T14:33:23Z | 2024-06-04T14:33:23Z | Label-wise Aleatoric and Epistemic Uncertainty Quantification | We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows uncertainty to be quantified at the individual class level, thereby improving cost-sensitive decision-making and helping understand the sources of uncertainty. Furthermore, it allows to define total, aleatoric, and epistemic uncertainty on the basis of non-categorical measures such as variance, going beyond common entropy-based measures. In particular, variance-based measures address some of the limitations associated with established methods that have recently been discussed in the literature. We show that our proposed measures adhere to a number of desirable properties. Through empirical evaluation on a variety of benchmark data sets -- including applications in the medical domain where accurate uncertainty quantification is crucial -- we establish the effectiveness of label-wise uncertainty quantification. | [
"['Yusuf Sale' 'Paul Hofman' 'Timo Löhr' 'Lisa Wimmer' 'Thomas Nagler'\n 'Eyke Hüllermeier']"
] |
null | null | 2406.02355 | null | null | http://arxiv.org/pdf/2406.02355v1 | 2024-06-04T14:34:13Z | 2024-06-04T14:34:13Z | FedDr+: Stabilizing Dot-regression with Global Feature Distillation for
Federated Learning | Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with heterogeneous, non-iid data distribution. A key challenge in FL is client drift, where data heterogeneity impedes the aggregation of scattered knowledge. Recent studies have tackled the client drift issue by identifying significant divergence in the last classifier layer. To mitigate this divergence, strategies such as freezing the classifier weights and aligning the feature extractor accordingly have proven effective. Although the local alignment between classifier and feature extractor has been studied as a crucial factor in FL, we observe that it may lead the model to overemphasize the observed classes within each client. Thus, our objectives are twofold: (1) enhancing local alignment while (2) preserving the representation of unseen class samples. This approach aims to effectively integrate knowledge from individual clients, thereby improving performance for both global and personalized FL. To achieve this, we introduce a novel algorithm named FedDr+, which empowers local model alignment using dot-regression loss. FedDr+ freezes the classifier as a simplex ETF to align the features and improves aggregated global models by employing a feature distillation mechanism to retain information about unseen/missing classes. Consequently, we provide empirical evidence demonstrating that our algorithm surpasses existing methods that use a frozen classifier to boost alignment across the diverse distribution. | [
"['Seongyoon Kim' 'Minchan Jeong' 'Sungnyun Kim' 'Sungwoo Cho'\n 'Sumyeong Ahn' 'Se-Young Yun']"
] |
null | null | 2406.02356 | null | null | http://arxiv.org/pdf/2406.02356v1 | 2024-06-04T14:34:39Z | 2024-06-04T14:34:39Z | Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy
Arithmetic Tasks | The ability (and inability) of large language models (LLMs) to perform arithmetic tasks has been the subject of much theoretical and practical debate. We show that LLMs are frequently able to correctly and confidently predict the first digit of n-digit by m-digit multiplication tasks without using chain of thought reasoning, despite these tasks require compounding operations to solve. Simultaneously, LLMs in practice often fail to correctly or confidently predict the last digit of an n-digit by m-digit multiplication, a task equivalent to 1-digit by 1-digit multiplication which can be easily learned or memorized. We show that the latter task can be solved more robustly when the LLM is conditioned on all of the correct higher-order digits, which on average increases the confidence of the correct last digit on 5-digit by 5-digit multiplication tasks using Llama 2-13B by over 230% (0.13 to 0.43) and Mistral-7B by 150% (0.22 to 0.55). | [
"['Andrew Gambardella' 'Yusuke Iwasawa' 'Yutaka Matsuo']"
] |
null | null | 2406.02357 | null | null | http://arxiv.org/pdf/2406.02357v1 | 2024-06-04T14:35:27Z | 2024-06-04T14:35:27Z | The complexity of approximate (coarse) correlated equilibrium for
incomplete information games | We study the iteration complexity of decentralized learning of approximate correlated equilibria in incomplete information games. On the negative side, we prove that in $mathit{extensive}$-$mathit{form}$ $mathit{games}$, assuming $mathsf{PPAD} notsubset mathsf{TIME}(n^{mathsf{polylog}(n)})$, any polynomial-time learning algorithms must take at least $2^{log_2^{1-o(1)}(|mathcal{I}|)}$ iterations to converge to the set of $epsilon$-approximate correlated equilibrium, where $|mathcal{I}|$ is the number of nodes in the game and $epsilon > 0$ is an absolute constant. This nearly matches, up to the $o(1)$ term, the algorithms of [PR'24, DDFG'24] for learning $epsilon$-approximate correlated equilibrium, and resolves an open question of Anagnostides, Kalavasis, Sandholm, and Zampetakis [AKSZ'24]. Our lower bound holds even for the easier solution concept of $epsilon$-approximate $mathit{coarse}$ correlated equilibrium On the positive side, we give uncoupled dynamics that reach $epsilon$-approximate correlated equilibria of a $mathit{Bayesian}$ $mathit{game}$ in polylogarithmic iterations, without any dependence of the number of types. This demonstrates a separation between Bayesian games and extensive-form games. | [
"['Binghui Peng' 'Aviad Rubinstein']"
] |
null | null | 2406.02361 | null | null | http://arxiv.org/pdf/2406.02361v1 | 2024-06-04T14:38:30Z | 2024-06-04T14:38:30Z | Using Self-supervised Learning Can Improve Model Fairness | Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Despite demonstrating comparable performance with supervised methods, comprehensive efforts to assess SSL's impact on machine learning fairness (i.e., performing equally on different demographic breakdowns) are lacking. Hypothesizing that SSL models would learn more generic, hence less biased representations, this study explores the impact of pre-training and fine-tuning strategies on fairness. We introduce a fairness assessment framework for SSL, comprising five stages: defining dataset requirements, pre-training, fine-tuning with gradual unfreezing, assessing representation similarity conditioned on demographics, and establishing domain-specific evaluation processes. We evaluate our method's generalizability on three real-world human-centric datasets (i.e., MIMIC, MESA, and GLOBEM) by systematically comparing hundreds of SSL and fine-tuned models on various dimensions spanning from the intermediate representations to appropriate evaluation metrics. Our findings demonstrate that SSL can significantly improve model fairness, while maintaining performance on par with supervised methods-exhibiting up to a 30% increase in fairness with minimal loss in performance through self-supervision. We posit that such differences can be attributed to representation dissimilarities found between the best- and the worst-performing demographics across models-up to x13 greater for protected attributes with larger performance discrepancies between segments. | [
"['Sofia Yfantidou' 'Dimitris Spathis' 'Marios Constantinides'\n 'Athena Vakali' 'Daniele Quercia' 'Fahim Kawsar']"
] |
null | null | 2406.02362 | null | null | http://arxiv.org/pdf/2406.02362v2 | 2024-06-05T11:22:46Z | 2024-06-04T14:39:51Z | Temporal Graph Rewiring with Expander Graphs | Evolving relations in real-world networks are often modelled by temporal graphs. Graph rewiring techniques have been utilised on Graph Neural Networks (GNNs) to improve expressiveness and increase model performance. In this work, we propose Temporal Graph Rewiring (TGR), the first approach for graph rewiring on temporal graphs. TGR enables communication between temporally distant nodes in a continuous time dynamic graph by utilising expander graph propagation to construct a message passing highway for message passing between distant nodes. Expander graphs are suitable candidates for rewiring as they help overcome the oversquashing problem often observed in GNNs. On the public tgbl-wiki benchmark, we show that TGR improves the performance of a widely used TGN model by a significant margin. Our code repository is accessible at https://github.com/kpetrovicc/TGR.git . | [
"['Katarina Petrović' 'Shenyang Huang' 'Farimah Poursafaei'\n 'Petar Veličković']"
] |
null | null | 2406.02366 | null | null | http://arxiv.org/pdf/2406.02366v1 | 2024-06-04T14:45:47Z | 2024-06-04T14:45:47Z | Finding NeMo: Localizing Neurons Responsible For Memorization in
Diffusion Models | Diffusion models (DMs) produce very detailed and high-quality images. Their power results from extensive training on large amounts of data, usually scraped from the internet without proper attribution or consent from content creators. Unfortunately, this practice raises privacy and intellectual property concerns, as DMs can memorize and later reproduce their potentially sensitive or copyrighted training images at inference time. Prior efforts prevent this issue by either changing the input to the diffusion process, thereby preventing the DM from generating memorized samples during inference, or removing the memorized data from training altogether. While those are viable solutions when the DM is developed and deployed in a secure and constantly monitored environment, they hold the risk of adversaries circumventing the safeguards and are not effective when the DM itself is publicly released. To solve the problem, we introduce NeMo, the first method to localize memorization of individual data samples down to the level of neurons in DMs' cross-attention layers. Through our experiments, we make the intriguing finding that in many cases, single neurons are responsible for memorizing particular training samples. By deactivating these memorization neurons, we can avoid the replication of training data at inference time, increase the diversity in the generated outputs, and mitigate the leakage of private and copyrighted data. In this way, our NeMo contributes to a more responsible deployment of DMs. | [
"['Dominik Hintersdorf' 'Lukas Struppek' 'Kristian Kersting'\n 'Adam Dziedzic' 'Franziska Boenisch']"
] |
null | null | 2406.02383 | null | null | http://arxiv.org/pdf/2406.02383v1 | 2024-06-04T14:59:38Z | 2024-06-04T14:59:38Z | Learning to Edit Visual Programs with Self-Supervision | We design a system that learns how to edit visual programs. Our edit network consumes a complete input program and a visual target. From this input, we task our network with predicting a local edit operation that could be applied to the input program to improve its similarity to the target. In order to apply this scheme for domains that lack program annotations, we develop a self-supervised learning approach that integrates this edit network into a bootstrapped finetuning loop along with a network that predicts entire programs in one-shot. Our joint finetuning scheme, when coupled with an inference procedure that initializes a population from the one-shot model and evolves members of this population with the edit network, helps to infer more accurate visual programs. Over multiple domains, we experimentally compare our method against the alternative of using only the one-shot model, and find that even under equal search-time budgets, our editing-based paradigm provides significant advantages. | [
"['R. Kenny Jones' 'Renhao Zhang' 'Aditya Ganeshan' 'Daniel Ritchie']"
] |
null | null | 2406.02394 | null | null | http://arxiv.org/pdf/2406.02394v1 | 2024-06-04T15:08:56Z | 2024-06-04T15:08:56Z | Multiple Choice Questions and Large Languages Models: A Case Study with
Fictional Medical Data | Large Language Models (LLMs) like ChatGPT demonstrate significant potential in the medical field, often evaluated using multiple-choice questions (MCQs) similar to those found on the USMLE. Despite their prevalence in medical education, MCQs have limitations that might be exacerbated when assessing LLMs. To evaluate the effectiveness of MCQs in assessing the performance of LLMs, we developed a fictional medical benchmark focused on a non-existent gland, the Glianorex. This approach allowed us to isolate the knowledge of the LLM from its test-taking abilities. We used GPT-4 to generate a comprehensive textbook on the Glianorex in both English and French and developed corresponding multiple-choice questions in both languages. We evaluated various open-source, proprietary, and domain-specific LLMs using these questions in a zero-shot setting. The models achieved average scores around 67%, with minor performance differences between larger and smaller models. Performance was slightly higher in English than in French. Fine-tuned medical models showed some improvement over their base versions in English but not in French. The uniformly high performance across models suggests that traditional MCQ-based benchmarks may not accurately measure LLMs' clinical knowledge and reasoning abilities, instead highlighting their pattern recognition skills. This study underscores the need for more robust evaluation methods to better assess the true capabilities of LLMs in medical contexts. | [
"['Maxime Griot' 'Jean Vanderdonckt' 'Demet Yuksel' 'Coralie Hemptinne']"
] |
null | null | 2406.02395 | null | null | http://arxiv.org/pdf/2406.02395v1 | 2024-06-04T15:09:29Z | 2024-06-04T15:09:29Z | GrootVL: Tree Topology is All You Need in State Space Model | The state space models, employing recursively propagated features, demonstrate strong representation capabilities comparable to Transformer models and superior efficiency. However, constrained by the inherent geometric constraints of sequences, it still falls short in modeling long-range dependencies. To address this issue, we propose the GrootVL network, which first dynamically generates a tree topology based on spatial relationships and input features. Then, feature propagation is performed based on this graph, thereby breaking the original sequence constraints to achieve stronger representation capabilities. Additionally, we introduce a linear complexity dynamic programming algorithm to enhance long-range interactions without increasing computational cost. GrootVL is a versatile multimodal framework that can be applied to both visual and textual tasks. Extensive experiments demonstrate that our method significantly outperforms existing structured state space models on image classification, object detection and segmentation. Besides, by fine-tuning large language models, our approach achieves consistent improvements in multiple textual tasks at minor training cost. | [
"['Yicheng Xiao' 'Lin Song' 'Shaoli Huang' 'Jiangshan Wang' 'Siyu Song'\n 'Yixiao Ge' 'Xiu Li' 'Ying Shan']"
] |
null | null | 2406.02416 | null | null | http://arxiv.org/pdf/2406.02416v1 | 2024-06-04T15:27:53Z | 2024-06-04T15:27:53Z | Improved Modelling of Federated Datasets using
Mixtures-of-Dirichlet-Multinomials | In practice, training using federated learning can be orders of magnitude slower than standard centralized training. This severely limits the amount of experimentation and tuning that can be done, making it challenging to obtain good performance on a given task. Server-side proxy data can be used to run training simulations, for instance for hyperparameter tuning. This can greatly speed up the training pipeline by reducing the number of tuning runs to be performed overall on the true clients. However, it is challenging to ensure that these simulations accurately reflect the dynamics of the real federated training. In particular, the proxy data used for simulations often comes as a single centralized dataset without a partition into distinct clients, and partitioning this data in a naive way can lead to simulations that poorly reflect real federated training. In this paper we address the challenge of how to partition centralized data in a way that reflects the statistical heterogeneity of the true federated clients. We propose a fully federated, theoretically justified, algorithm that efficiently learns the distribution of the true clients and observe improved server-side simulations when using the inferred distribution to create simulated clients from the centralized data. | [
"['Jonathan Scott' 'Áine Cahill']"
] |
null | null | 2406.02421 | null | null | http://arxiv.org/pdf/2406.02421v1 | 2024-06-04T15:39:08Z | 2024-06-04T15:39:08Z | Representing Piecewise-Linear Functions by Functions with Minimal Arity | Any continuous piecewise-linear function $Fcolon mathbb{R}^{n}to mathbb{R}$ can be represented as a linear combination of $max$ functions of at most $n+1$ affine-linear functions. In our previous paper [``Representing piecewise linear functions by functions with small arity'', AAECC, 2023], we showed that this upper bound of $n+1$ arguments is tight. In the present paper, we extend this result by establishing a correspondence between the function $F$ and the minimal number of arguments that are needed in any such decomposition. We show that the tessellation of the input space $mathbb{R}^{n}$ induced by the function $F$ has a direct connection to the number of arguments in the $max$ functions. | [
"['Christoph Koutschan' 'Anton Ponomarchuk' 'Josef Schicho']"
] |
null | null | 2406.02422 | null | null | http://arxiv.org/pdf/2406.02422v2 | 2024-06-05T13:17:23Z | 2024-06-04T15:39:49Z | IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and
Frequency Masking for Brain Lesions in MRI | Unsupervised anomaly segmentation approaches to pathology segmentation train a model on images of healthy subjects, that they define as the 'normal' data distribution. At inference, they aim to segment any pathologies in new images as 'anomalies', as they exhibit patterns that deviate from those in 'normal' training data. Prevailing methods follow the 'corrupt-and-reconstruct' paradigm. They intentionally corrupt an input image, reconstruct it to follow the learned 'normal' distribution, and subsequently segment anomalies based on reconstruction error. Corrupting an input image, however, inevitably leads to suboptimal reconstruction even of normal regions, causing false positives. To alleviate this, we propose a novel iterative spatial mask-refining strategy IterMask2. We iteratively mask areas of the image, reconstruct them, and update the mask based on reconstruction error. This iterative process progressively adds information about areas that are confidently normal as per the model. The increasing content guides reconstruction of nearby masked areas, improving reconstruction of normal tissue under these areas, reducing false positives. We also use high-frequency image content as an auxiliary input to provide additional structural information for masked areas. This further improves reconstruction error of normal in comparison to anomalous areas, facilitating segmentation of the latter. We conduct experiments on several brain lesion datasets and demonstrate effectiveness of our method. Code is available at: https://github.com/ZiyunLiang/IterMask2 | [
"['Ziyun Liang' 'Xiaoqing Guo' 'J. Alison Noble' 'Konstantinos Kamnitsas']"
] |
null | null | 2406.02424 | null | null | http://arxiv.org/pdf/2406.02424v1 | 2024-06-04T15:44:10Z | 2024-06-04T15:44:10Z | Contextual Dynamic Pricing: Algorithms, Optimality, and Local
Differential Privacy Constraints | We study the contextual dynamic pricing problem where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model. The firm aims to maximize its revenue, i.e. minimize its regret over a clairvoyant that knows the model in advance. The demand model is a generalized linear model (GLM), allowing for a stochastic feature vector in $mathbb R^d$ that encodes product and consumer information. We first show that the optimal regret upper bound is of order $sqrt{dT}$, up to a logarithmic factor, improving upon existing upper bounds in the literature by a $sqrt{d}$ factor. This sharper rate is materialised by two algorithms: a confidence bound-type (supCB) algorithm and an explore-then-commit (ETC) algorithm. A key insight of our theoretical result is an intrinsic connection between dynamic pricing and the contextual multi-armed bandit problem with many arms based on a careful discretization. We further study contextual dynamic pricing under the local differential privacy (LDP) constraints. In particular, we propose a stochastic gradient descent based ETC algorithm that achieves an optimal regret upper bound of order $dsqrt{T}/epsilon$, up to a logarithmic factor, where $epsilon>0$ is the privacy parameter. The regret upper bounds with and without LDP constraints are accompanied by newly constructed minimax lower bounds, which further characterize the cost of privacy. Extensive numerical experiments and a real data application on online lending are conducted to illustrate the efficiency and practical value of the proposed algorithms in dynamic pricing. | [
"['Zifeng Zhao' 'Feiyu Jiang' 'Yi Yu']"
] |
null | null | 2406.02426 | null | null | http://arxiv.org/pdf/2406.02426v1 | 2024-06-04T15:46:41Z | 2024-06-04T15:46:41Z | Contextual Optimization under Covariate Shift: A Robust Approach by
Intersecting Wasserstein Balls | In contextual optimization, a decision-maker observes historical samples of uncertain variables and associated concurrent covariates, without knowing their joint distribution. Given an additional covariate observation, the goal is to choose a decision that minimizes some operational costs. A prevalent issue here is covariate shift, where the marginal distribution of the new covariate differs from historical samples, leading to decision performance variations with nonparametric or parametric estimators. To address this, we propose a distributionally robust approach that uses an ambiguity set by the intersection of two Wasserstein balls, each centered on typical nonparametric or parametric distribution estimators. Computationally, we establish the tractable reformulation of this distributionally robust optimization problem. Statistically, we provide guarantees for our Wasserstein ball intersection approach under covariate shift by analyzing the measure concentration of the estimators. Furthermore, to reduce computational complexity, we employ a surrogate objective that maintains similar generalization guarantees. Through synthetic and empirical case studies on income prediction and portfolio optimization, we demonstrate the strong empirical performance of our proposed models. | [
"['Tianyu Wang' 'Ningyuan Chen' 'Chun Wang']"
] |
null | null | 2406.02428 | null | null | http://arxiv.org/pdf/2406.02428v1 | 2024-06-04T15:47:03Z | 2024-06-04T15:47:03Z | Harnessing Neural Unit Dynamics for Effective and Scalable
Class-Incremental Learning | Class-incremental learning (CIL) aims to train a model to learn new classes from non-stationary data streams without forgetting old ones. In this paper, we propose a new kind of connectionist model by tailoring neural unit dynamics that adapt the behavior of neural networks for CIL. In each training session, it introduces a supervisory mechanism to guide network expansion whose growth size is compactly commensurate with the intrinsic complexity of a newly arriving task. This constructs a near-minimal network while allowing the model to expand its capacity when cannot sufficiently hold new classes. At inference time, it automatically reactivates the required neural units to retrieve knowledge and leaves the remaining inactivated to prevent interference. We name our model AutoActivator, which is effective and scalable. To gain insights into the neural unit dynamics, we theoretically analyze the model's convergence property via a universal approximation theorem on learning sequential mappings, which is under-explored in the CIL community. Experiments show that our method achieves strong CIL performance in rehearsal-free and minimal-expansion settings with different backbones. | [
"['Depeng Li' 'Tianqi Wang' 'Junwei Chen' 'Wei Dai' 'Zhigang Zeng']"
] |
null | null | 2406.02431 | null | null | http://arxiv.org/pdf/2406.02431v1 | 2024-06-04T15:50:35Z | 2024-06-04T15:50:35Z | Reweighted Solutions for Weighted Low Rank Approximation | Weighted low rank approximation (WLRA) is an important yet computationally challenging primitive with applications ranging from statistical analysis, model compression, and signal processing. To cope with the NP-hardness of this problem, prior work considers heuristics, bicriteria, or fixed parameter tractable algorithms to solve this problem. In this work, we introduce a new relaxed solution to WLRA which outputs a matrix that is not necessarily low rank, but can be stored using very few parameters and gives provable approximation guarantees when the weight matrix has low rank. Our central idea is to use the weight matrix itself to reweight a low rank solution, which gives an extremely simple algorithm with remarkable empirical performance in applications to model compression and on synthetic datasets. Our algorithm also gives nearly optimal communication complexity bounds for a natural distributed problem associated with this problem, for which we show matching communication lower bounds. Together, our communication complexity bounds show that the rank of the weight matrix provably parameterizes the communication complexity of WLRA. We also obtain the first relative error guarantees for feature selection with a weighted objective. | [
"['David P. Woodruff' 'Taisuke Yasuda']"
] |
null | null | 2406.02432 | null | null | http://arxiv.org/pdf/2406.02432v1 | 2024-06-04T15:50:42Z | 2024-06-04T15:50:42Z | Coresets for Multiple $\ell_p$ Regression | A coreset of a dataset with $n$ examples and $d$ features is a weighted subset of examples that is sufficient for solving downstream data analytic tasks. Nearly optimal constructions of coresets for least squares and $ell_p$ linear regression with a single response are known in prior work. However, for multiple $ell_p$ regression where there can be $m$ responses, there are no known constructions with size sublinear in $m$. In this work, we construct coresets of size $tilde O(varepsilon^{-2}d)$ for $p<2$ and $tilde O(varepsilon^{-p}d^{p/2})$ for $p>2$ independently of $m$ (i.e., dimension-free) that approximate the multiple $ell_p$ regression objective at every point in the domain up to $(1pmvarepsilon)$ relative error. If we only need to preserve the minimizer subject to a subspace constraint, we improve these bounds by an $varepsilon$ factor for all $p>1$. All of our bounds are nearly tight. We give two application of our results. First, we settle the number of uniform samples needed to approximate $ell_p$ Euclidean power means up to a $(1+varepsilon)$ factor, showing that $tildeTheta(varepsilon^{-2})$ samples for $p = 1$, $tildeTheta(varepsilon^{-1})$ samples for $1 < p < 2$, and $tildeTheta(varepsilon^{1-p})$ samples for $p>2$ is tight, answering a question of Cohen-Addad, Saulpic, and Schwiegelshohn. Second, we show that for $1<p<2$, every matrix has a subset of $tilde O(varepsilon^{-1}k)$ rows which spans a $(1+varepsilon)$-approximately optimal $k$-dimensional subspace for $ell_p$ subspace approximation, which is also nearly optimal. | [
"['David P. Woodruff' 'Taisuke Yasuda']"
] |
null | null | 2406.02447 | null | null | http://arxiv.org/pdf/2406.02447v1 | 2024-06-04T16:12:27Z | 2024-06-04T16:12:27Z | Reducing Bias in Federated Class-Incremental Learning with Hierarchical
Generative Prototypes | Federated Learning (FL) aims at unburdening the training of deep models by distributing computation across multiple devices (clients) while safeguarding data privacy. On top of that, Federated Continual Learning (FCL) also accounts for data distribution evolving over time, mirroring the dynamic nature of real-world environments. In this work, we shed light on the Incremental and Federated biases that naturally emerge in FCL. While the former is a known problem in Continual Learning, stemming from the prioritization of recently introduced classes, the latter (i.e., the bias towards local distributions) remains relatively unexplored. Our proposal constrains both biases in the last layer by efficiently fine-tuning a pre-trained backbone using learnable prompts, resulting in clients that produce less biased representations and more biased classifiers. Therefore, instead of solely relying on parameter aggregation, we also leverage generative prototypes to effectively balance the predictions of the global model. Our method improves on the current State Of The Art, providing an average increase of +7.9% in accuracy. | [
"['Riccardo Salami' 'Pietro Buzzega' 'Matteo Mosconi' 'Mattia Verasani'\n 'Simone Calderara']"
] |
null | null | 2406.02450 | null | null | http://arxiv.org/pdf/2406.02450v1 | 2024-06-04T16:14:55Z | 2024-06-04T16:14:55Z | A Generalized Apprenticeship Learning Framework for Modeling
Heterogeneous Student Pedagogical Strategies | A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) often suffers from sample inefficiency and reward function design difficulty, Apprenticeship Learning(AL) algorithms can overcome them. However, most AL algorithms can not handle heterogeneity as they assume all demonstrations are generated with a homogeneous policy driven by a single reward function. Still, some AL algorithms which consider heterogeneity, often can not generalize to large continuous state space and only work with discrete states. In this paper, we propose an expectation-maximization(EM)-EDM, a general AL framework to induce effective pedagogical policies from given optimal or near-optimal demonstrations, which are assumed to be driven by heterogeneous reward functions. We compare the effectiveness of the policies induced by our proposed EM-EDM against four AL-based baselines and two policies induced by DRL on two different but related tasks that involve pedagogical action prediction. Our overall results showed that, for both tasks, EM-EDM outperforms the four AL baselines across all performance metrics and the two DRL baselines. This suggests that EM-EDM can effectively model complex student pedagogical decision-making processes through the ability to manage a large, continuous state space and adapt to handle diverse and heterogeneous reward functions with very few given demonstrations. | [
"['Md Mirajul Islam' 'Xi Yang' 'John Hostetter' 'Adittya Soukarjya Saha'\n 'Min Chi']"
] |
null | null | 2406.02456 | null | null | http://arxiv.org/pdf/2406.02456v1 | 2024-06-04T16:21:14Z | 2024-06-04T16:21:14Z | Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and
Posterior Value Optimisation in Finite-State MDPs | We address the challenge of quantifying Bayesian uncertainty and incorporating it in offline use cases of finite-state Markov Decision Processes (MDPs) with unknown dynamics. Our approach provides a principled method to disentangle epistemic and aleatoric uncertainty, and a novel technique to find policies that optimise Bayesian posterior expected value without relying on strong assumptions about the MDP's posterior distribution. First, we utilise standard Bayesian reinforcement learning methods to capture the posterior uncertainty in MDP parameters based on available data. We then analytically compute the first two moments of the return distribution across posterior samples and apply the law of total variance to disentangle aleatoric and epistemic uncertainties. To find policies that maximise posterior expected value, we leverage the closed-form expression for value as a function of policy. This allows us to propose a stochastic gradient-based approach for solving the problem. We illustrate the uncertainty quantification and Bayesian posterior value optimisation performance of our agent in simple, interpretable gridworlds and validate it through ground-truth evaluations on synthetic MDPs. Finally, we highlight the real-world impact and computational scalability of our method by applying it to the AI Clinician problem, which recommends treatment for patients in intensive care units and has emerged as a key use case of finite-state MDPs with offline data. We discuss the challenges that arise with Bayesian modelling of larger scale MDPs while demonstrating the potential to apply our methods rooted in Bayesian decision theory into the real world. We make our code available at https://github.com/filippovaldettaro/finite-state-mdps . | [
"['Filippo Valdettaro' 'A. Aldo Faisal']"
] |
null | null | 2406.02457 | null | null | http://arxiv.org/pdf/2406.02457v1 | 2024-06-04T16:21:24Z | 2024-06-04T16:21:24Z | Machine learning Hubbard parameters with equivariant neural networks | Density-functional theory with extended Hubbard functionals (DFT+$U$+$V$) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled $d$ and $f$ electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site $U$ and inter-site $V$ Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. Here, we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors, directly capturing the electronic structure, local chemical environment, and oxidation states of the system at hand. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations. Remarkably, when trained on data from 11 materials spanning various crystal structures and compositions, our model achieves mean absolute relative errors of 3% and 5% for Hubbard $U$ and $V$ parameters, respectively. By circumventing computationally expensive DFT or DFPT self-consistent protocols, our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead, while approaching the accuracy of DFPT. Moreover, owing to its robust transferability, the model facilitates accelerated materials discovery and design via high-throughput calculations, with relevance for various technological applications. | [
"['Martin Uhrin' 'Austin Zadoks' 'Luca Binci' 'Nicola Marzari'\n 'Iurii Timrov']"
] |
null | null | 2406.02464 | null | null | http://arxiv.org/pdf/2406.02464v1 | 2024-06-04T16:31:43Z | 2024-06-04T16:31:43Z | Meta-Learners for Partially-Identified Treatment Effects Across Multiple
Environments | Estimating the conditional average treatment effect (CATE) from observational data is relevant for many applications such as personalized medicine. Here, we focus on the widespread setting where the observational data come from multiple environments, such as different hospitals, physicians, or countries. Furthermore, we allow for violations of standard causal assumptions, namely, overlap within the environments and unconfoundedness. To this end, we move away from point identification and focus on partial identification. Specifically, we show that current assumptions from the literature on multiple environments allow us to interpret the environment as an instrumental variable (IV). This allows us to adapt bounds from the IV literature for partial identification of CATE by leveraging treatment assignment mechanisms across environments. Then, we propose different model-agnostic learners (so-called meta-learners) to estimate the bounds that can be used in combination with arbitrary machine learning models. We further demonstrate the effectiveness of our meta-learners across various experiments using both simulated and real-world data. Finally, we discuss the applicability of our meta-learners to partial identification in instrumental variable settings, such as randomized controlled trials with non-compliance. | [
"['Jonas Schweisthal' 'Dennis Frauen' 'Mihaela van der Schaar'\n 'Stefan Feuerriegel']"
] |
null | null | 2406.02465 | null | null | http://arxiv.org/pdf/2406.02465v1 | 2024-06-04T16:34:17Z | 2024-06-04T16:34:17Z | An Empirical Study into Clustering of Unseen Datasets with
Self-Supervised Encoders | Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embeddings form meaningful clusters. Our suite of benchmarking experiments use encoders pretrained solely on ImageNet-1k with either supervised or self-supervised training techniques, deployed on image datasets that were not seen during training, and clustered with conventional clustering algorithms. This evaluation provides new insights into the embeddings of self-supervised models, which prioritize different features to supervised models. Supervised encoders typically offer more utility than SSL encoders within the training domain, and vice-versa far outside of it, however, fine-tuned encoders demonstrate the opposite trend. Clustering provides a way to evaluate the utility of self-supervised learned representations orthogonal to existing methods such as kNN. Additionally, we find the silhouette score when measured in a UMAP-reduced space is highly correlated with clustering performance, and can therefore be used as a proxy for clustering performance on data with no ground truth labels. Our code implementation is available at url{https://github.com/scottclowe/zs-ssl-clustering/}. | [
"['Scott C. Lowe' 'Joakim Bruslund Haurum' 'Sageev Oore'\n 'Thomas B. Moeslund' 'Graham W. Taylor']"
] |
null | null | 2406.02469 | null | null | http://arxiv.org/pdf/2406.02469v1 | 2024-06-04T16:38:57Z | 2024-06-04T16:38:57Z | Landscape-Aware Growing: The Power of a Little LAG | Recently, there has been increasing interest in efficient pretraining paradigms for training Transformer-based models. Several recent approaches use smaller models to initialize larger models in order to save computation (e.g., stacking and fusion). In this work, we study the fundamental question of how to select the best growing strategy from a given pool of growing strategies. Prior works have extensively focused on loss- and/or function-preserving behavior at initialization or simply performance at the end of training. Instead, we identify that behavior at initialization can be misleading as a predictor of final performance and present an alternative perspective based on early training dynamics, which we call "landscape-aware growing (LAG)". We perform extensive analysis of correlation of the final performance with performance in the initial steps of training and find early and more accurate predictions of the optimal growing strategy (i.e., with only a small "lag" after initialization). This perspective also motivates an adaptive strategy for gradual stacking. | [
"['Stefani Karp' 'Nikunj Saunshi' 'Sobhan Miryoosefi' 'Sashank J. Reddi'\n 'Sanjiv Kumar']"
] |
null | null | 2406.02470 | null | null | http://arxiv.org/pdf/2406.02470v1 | 2024-06-04T16:40:55Z | 2024-06-04T16:40:55Z | Meta-Designing Quantum Experiments with Language Models | Artificial Intelligence (AI) has the potential to significantly advance scientific discovery by finding solutions beyond human capabilities. However, these super-human solutions are often unintuitive and require considerable effort to uncover underlying principles, if possible at all. Here, we show how a code-generating language model trained on synthetic data can not only find solutions to specific problems but can create meta-solutions, which solve an entire class of problems in one shot and simultaneously offer insight into the underlying design principles. Specifically, for the design of new quantum physics experiments, our sequence-to-sequence transformer architecture generates interpretable Python code that describes experimental blueprints for a whole class of quantum systems. We discover general and previously unknown design rules for infinitely large classes of quantum states. The ability to automatically generate generalized patterns in readable computer code is a crucial step toward machines that help discover new scientific understanding -- one of the central aims of physics. | [
"['Sören Arlt' 'Haonan Duan' 'Felix Li' 'Sang Michael Xie' 'Yuhuai Wu'\n 'Mario Krenn']"
] |
null | null | 2406.02477 | null | null | http://arxiv.org/pdf/2406.02477v1 | 2024-06-04T16:47:47Z | 2024-06-04T16:47:47Z | Inpainting Pathology in Lumbar Spine MRI with Latent Diffusion | Data driven models for automated diagnosis in radiology suffer from insufficient and imbalanced datasets due to low representation of pathology in a population and the cost of expert annotations. Datasets can be bolstered through data augmentation. However, even when utilizing a full suite of transformations during model training, typical data augmentations do not address variations in human anatomy. An alternative direction is to synthesize data using generative models, which can potentially craft datasets with specific attributes. While this holds promise, commonly used generative models such as Generative Adversarial Networks may inadvertently produce anatomically inaccurate features. On the other hand, diffusion models, which offer greater stability, tend to memorize training data, raising concerns about privacy and generative diversity. Alternatively, inpainting has the potential to augment data through directly inserting pathology in medical images. However, this approach introduces a new challenge: accurately merging the generated pathological features with the surrounding anatomical context. While inpainting is a well established method for addressing simple lesions, its application to pathologies that involve complex structural changes remains relatively unexplored. We propose an efficient method for inpainting pathological features onto healthy anatomy in MRI through voxelwise noise scheduling in a latent diffusion model. We evaluate the method's ability to insert disc herniation and central canal stenosis in lumbar spine sagittal T2 MRI, and it achieves superior Frechet Inception Distance compared to state-of-the-art methods. | [
"['Colin Hansen' 'Simas Glinskis' 'Ashwin Raju' 'Micha Kornreich'\n 'JinHyeong Park' 'Jayashri Pawar' 'Richard Herzog' 'Li Zhang'\n 'Benjamin Odry']"
] |
null | null | 2406.02479 | null | null | http://arxiv.org/pdf/2406.02479v1 | 2024-06-02T23:18:11Z | 2024-06-02T23:18:11Z | Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile
Analysis | This paper presents a novel method for utilizing fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis, demonstrated through the restoration of missing data in power system load profiles. A two-stage fine-tuning strategy is proposed to adapt a pre-trained LLMs, i.e., GPT-3.5, for missing data restoration tasks. Through empirical evaluation, we demonstrate the effectiveness of the fine-tuned model in accurately restoring missing data, achieving comparable performance to state-of-the-art specifically designed models such as BERT-PIN. Key findings include the importance of prompt engineering and the optimal utilization of fine-tuning samples, highlighting the efficiency of few-shot learning in transferring knowledge from general user cases to specific target users. Furthermore, the proposed approach demonstrates notable cost-effectiveness and time efficiency compared to training models from scratch, making it a practical solution for scenarios with limited data availability and computing resources. This research has significant potential for application to other power system load profile analysis tasks. Consequently, it advances the use of LLMs in power system analytics, offering promising implications for enhancing the resilience and efficiency of power distribution systems. | [
"['Yi Hu' 'Hyeonjin Kim' 'Kai Ye' 'Ning Lu']"
] |
null | null | 2406.02486 | null | null | http://arxiv.org/pdf/2406.02486v2 | 2024-06-05T16:32:16Z | 2024-06-04T16:55:42Z | A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting | Capturing complex temporal patterns and relationships within multivariate data streams is a difficult task. We propose the Temporal Kolmogorov-Arnold Transformer (TKAT), a novel attention-based architecture designed to address this task using Temporal Kolmogorov-Arnold Networks (TKANs). Inspired by the Temporal Fusion Transformer (TFT), TKAT emerges as a powerful encoder-decoder model tailored to handle tasks in which the observed part of the features is more important than the a priori known part. This new architecture combined the theoretical foundation of the Kolmogorov-Arnold representation with the power of transformers. TKAT aims to simplify the complex dependencies inherent in time series, making them more "interpretable". The use of transformer architecture in this framework allows us to capture long-range dependencies through self-attention mechanisms. | [
"['Remi Genet' 'Hugo Inzirillo']"
] |
null | null | 2406.02490 | null | null | http://arxiv.org/pdf/2406.02490v1 | 2024-06-04T17:00:14Z | 2024-06-04T17:00:14Z | Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps | Markov chain Monte Carlo methods have become popular in statistics as versatile techniques to sample from complicated probability distributions. In this work, we propose a method to parameterize and train transition kernels of Markov chains to achieve efficient sampling and good mixing. This training procedure minimizes the total variation distance between the stationary distribution of the chain and the empirical distribution of the data. Our approach leverages involutive Metropolis-Hastings kernels constructed from reversible neural networks that ensure detailed balance by construction. We find that reversibility also implies $C_2$-equivariance of the discriminator function which can be used to restrict its function space. | [
"['Evgenii Egorov' 'Ricardo Valperga' 'Efstratios Gavves']"
] |
null | null | 2406.02496 | null | null | http://arxiv.org/pdf/2406.02496v1 | 2024-06-04T17:14:31Z | 2024-06-04T17:14:31Z | Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power
and Interpretability | Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the MIT team, representing a revolutionary approach with the potential to be a game-changer in the field. This innovative concept has rapidly garnered worldwide interest within the AI community. Inspired by the Kolmogorov-Arnold representation theorem, KAN utilizes spline-parametrized univariate functions in place of traditional linear weights, enabling them to dynamically learn activation patterns and significantly enhancing interpretability. In this paper, we explore the application of KAN to time series forecasting and propose two variants: T-KAN and MT-KAN. T-KAN is designed to detect concept drift within time series and can explain the nonlinear relationships between predictions and previous time steps through symbolic regression, making it highly interpretable in dynamically changing environments. MT-KAN, on the other hand, improves predictive performance by effectively uncovering and leveraging the complex relationships among variables in multivariate time series. Experiments validate the effectiveness of these approaches, demonstrating that T-KAN and MT-KAN significantly outperform traditional methods in time series forecasting tasks, not only enhancing predictive accuracy but also improving model interpretability. This research opens new avenues for adaptive forecasting models, highlighting the potential of KAN as a powerful and interpretable tool in predictive analytics. | [
"['Kunpeng Xu' 'Lifei Chen' 'Shengrui Wang']"
] |
null | null | 2406.02497 | null | null | http://arxiv.org/pdf/2406.02497v1 | 2024-06-04T17:15:25Z | 2024-06-04T17:15:25Z | Dropout MPC: An Ensemble Neural MPC Approach for Systems with Learned
Dynamics | Neural networks are lately more and more often being used in the context of data-driven control, as an approximate model of the true system dynamics. Model Predictive Control (MPC) adopts this practise leading to neural MPC strategies. This raises a question of whether the trained neural network has converged and generalized in a way that the learned model encapsulates an accurate approximation of the true dynamic model of the system, thus making it a reliable choice for model-based control, especially for disturbed and uncertain systems. To tackle that, we propose Dropout MPC, a novel sampling-based ensemble neural MPC algorithm that employs the Monte-Carlo dropout technique on the learned system model. The closed loop is based on an ensemble of predictive controllers, that are used simultaneously at each time-step for trajectory optimization. Each member of the ensemble influences the control input, based on a weighted voting scheme, thus by employing different realizations of the learned system dynamics, neural control becomes more reliable by design. An additional strength of the method is that it offers by design a way to estimate future uncertainty, leading to cautious control. While the method aims in general at uncertain systems with complex dynamics, where models derived from first principles are hard to infer, to showcase the application we utilize data gathered in the laboratory from a real mobile manipulator and employ the proposed algorithm for the navigation of the robot in simulation. | [
"['Spyridon Syntakas' 'Kostas Vlachos']"
] |
null | null | 2406.02500 | null | null | http://arxiv.org/pdf/2406.02500v2 | 2024-06-24T21:51:23Z | 2024-06-04T17:18:40Z | Demystifying the Compression of Mixture-of-Experts Through a Unified
Framework | Scaling large language models has revolutionized the performance across diverse domains, yet the continual growth in model size poses significant challenges for real-world deployment. The Mixture of Experts (MoE) approach addresses this by dynamically selecting and activating only a subset of experts, significantly reducing computational costs while maintaining high performance. However, MoE introduces potential redundancy (e.g., parameters) and extra costs (e.g., communication overhead). Despite numerous compression techniques developed for mitigating the redundancy in dense models, the compression of MoE remains under-explored. We first bridge this gap with a cutting-edge unified framework that not only seamlessly integrates mainstream compression methods but also helps systematically understand MoE compression. This framework approaches compression from two perspectives: Expert Slimming which compresses individual experts and Expert Trimming which removes structured modules. Within this framework, we explore the optimization space unexplored by existing methods,and further introduce aggressive Expert Trimming techniques, i.e., Layer Drop and Block Drop, to eliminate redundancy at larger scales. Based on these insights,we present a comprehensive recipe to guide practitioners in compressing MoE effectively. Extensive experimental results demonstrate the effectiveness of the compression methods under our framework and the proposed recipe, achieving a 6.05x speedup and only 20.0GB memory usage while maintaining over 92% of performance on Mixtral-8x7B. Code is released at url{https://github.com/DaizeDong/Unified-MoE-Compression}. | [
"['Shwai He' 'Daize Dong' 'Liang Ding' 'Ang Li']"
] |
null | null | 2406.02507 | null | null | http://arxiv.org/pdf/2406.02507v1 | 2024-06-04T17:25:59Z | 2024-06-04T17:25:59Z | Guiding a Diffusion Model with a Bad Version of Itself | The primary axes of interest in image-generating diffusion models are image quality, the amount of variation in the results, and how well the results align with a given condition, e.g., a class label or a text prompt. The popular classifier-free guidance approach uses an unconditional model to guide a conditional model, leading to simultaneously better prompt alignment and higher-quality images at the cost of reduced variation. These effects seem inherently entangled, and thus hard to control. We make the surprising observation that it is possible to obtain disentangled control over image quality without compromising the amount of variation by guiding generation using a smaller, less-trained version of the model itself rather than an unconditional model. This leads to significant improvements in ImageNet generation, setting record FIDs of 1.01 for 64x64 and 1.25 for 512x512, using publicly available networks. Furthermore, the method is also applicable to unconditional diffusion models, drastically improving their quality. | [
"['Tero Karras' 'Miika Aittala' 'Tuomas Kynkäänniemi' 'Jaakko Lehtinen'\n 'Timo Aila' 'Samuli Laine']"
] |
null | null | 2406.02510 | null | null | http://arxiv.org/pdf/2406.02510v1 | 2024-06-04T17:29:21Z | 2024-06-04T17:29:21Z | Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream
Predictive Tasks | Among various aspects of ensuring the responsible design of AI tools for healthcare applications, addressing fairness concerns has been a key focus area. Specifically, given the wide spread of electronic health record (EHR) data and their huge potential to inform a wide range of clinical decision support tasks, improving fairness in this category of health AI tools is of key importance. While such a broad problem (that is, mitigating fairness in EHR-based AI models) has been tackled using various methods, task- and model-agnostic methods are noticeably rare. In this study, we aimed to target this gap by presenting a new pipeline that generates synthetic EHR data, which is not only consistent with (faithful to) the real EHR data but also can reduce the fairness concerns (defined by the end-user) in the downstream tasks, when combined with the real data. We demonstrate the effectiveness of our proposed pipeline across various downstream tasks and two different EHR datasets. Our proposed pipeline can add a widely applicable and complementary tool to the existing toolbox of methods to address fairness in health AI applications such as those modifying the design of a downstream model. The codebase for our project is available at https://github.com/healthylaife/FairSynth | [
"['Mirza Farhan Bin Tarek' 'Raphael Poulain' 'Rahmatollah Beheshti']"
] |
null | null | 2406.02515 | null | null | http://arxiv.org/pdf/2406.02515v1 | 2024-06-04T17:38:24Z | 2024-06-04T17:38:24Z | Uncertainty of Joint Neural Contextual Bandit | Contextual bandit learning is increasingly favored in modern large-scale recommendation systems. To better utlize the contextual information and available user or item features, the integration of neural networks have been introduced to enhance contextual bandit learning and has triggered significant interest from both academia and industry. However, a major challenge arises when implementing a disjoint neural contextual bandit solution in large-scale recommendation systems, where each item or user may correspond to a separate bandit arm. The huge number of items to recommend poses a significant hurdle for real world production deployment. This paper focuses on a joint neural contextual bandit solution which serves all recommending items in one single model. The output consists of a predicted reward $mu$, an uncertainty $sigma$ and a hyper-parameter $alpha$ which balances exploitation and exploration, e.g., $mu + alpha sigma$. The tuning of the parameter $alpha$ is typically heuristic and complex in practice due to its stochastic nature. To address this challenge, we provide both theoretical analysis and experimental findings regarding the uncertainty $sigma$ of the joint neural contextual bandit model. Our analysis reveals that $alpha$ demonstrates an approximate square root relationship with the size of the last hidden layer $F$ and inverse square root relationship with the amount of training data $N$, i.e., $sigma propto sqrt{frac{F}{N}}$. The experiments, conducted with real industrial data, align with the theoretical analysis, help understanding model behaviors and assist the hyper-parameter tuning during both offline training and online deployment. | [
"['Hongbo Guo' 'Zheqing Zhu']"
] |
null | null | 2406.02523 | null | null | http://arxiv.org/pdf/2406.02523v1 | 2024-06-04T17:41:31Z | 2024-06-04T17:41:31Z | RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots | Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate using realistic physical simulation as a means to scale environments, tasks, and datasets for robot learning methods. We present RoboCasa, a large-scale simulation framework for training generalist robots in everyday environments. RoboCasa features realistic and diverse scenes focusing on kitchen environments. We provide thousands of 3D assets across over 150 object categories and dozens of interactable furniture and appliances. We enrich the realism and diversity of our simulation with generative AI tools, such as object assets from text-to-3D models and environment textures from text-to-image models. We design a set of 100 tasks for systematic evaluation, including composite tasks generated by the guidance of large language models. To facilitate learning, we provide high-quality human demonstrations and integrate automated trajectory generation methods to substantially enlarge our datasets with minimal human burden. Our experiments show a clear scaling trend in using synthetically generated robot data for large-scale imitation learning and show great promise in harnessing simulation data in real-world tasks. Videos and open-source code are available at https://robocasa.ai/ | [
"['Soroush Nasiriany' 'Abhiram Maddukuri' 'Lance Zhang' 'Adeet Parikh'\n 'Aaron Lo' 'Abhishek Joshi' 'Ajay Mandlekar' 'Yuke Zhu']"
] |
null | null | 2406.02529 | null | null | http://arxiv.org/pdf/2406.02529v1 | 2024-06-04T17:51:08Z | 2024-06-04T17:51:08Z | ReLUs Are Sufficient for Learning Implicit Neural Representations | Motivated by the growing theoretical understanding of neural networks that employ the Rectified Linear Unit (ReLU) as their activation function, we revisit the use of ReLU activation functions for learning implicit neural representations (INRs). Inspired by second order B-spline wavelets, we incorporate a set of simple constraints to the ReLU neurons in each layer of a deep neural network (DNN) to remedy the spectral bias. This in turn enables its use for various INR tasks. Empirically, we demonstrate that, contrary to popular belief, one can learn state-of-the-art INRs based on a DNN composed of only ReLU neurons. Next, by leveraging recent theoretical works which characterize the kinds of functions ReLU neural networks learn, we provide a way to quantify the regularity of the learned function. This offers a principled approach to selecting the hyperparameters in INR architectures. We substantiate our claims through experiments in signal representation, super resolution, and computed tomography, demonstrating the versatility and effectiveness of our method. The code for all experiments can be found at https://github.com/joeshenouda/relu-inrs. | [
"['Joseph Shenouda' 'Yamin Zhou' 'Robert D. Nowak']"
] |
null | null | 2406.02534 | null | null | http://arxiv.org/pdf/2406.02534v1 | 2024-06-04T17:54:44Z | 2024-06-04T17:54:44Z | Enhancing predictive imaging biomarker discovery through treatment
effect analysis | Identifying predictive biomarkers, which forecast individual treatment effectiveness, is crucial for personalized medicine and informs decision-making across diverse disciplines. These biomarkers are extracted from pre-treatment data, often within randomized controlled trials, and have to be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on the discovery of predictive imaging biomarkers, aiming to leverage pre-treatment images to unveil new causal relationships. Previous approaches relied on labor-intensive handcrafted or manually derived features, which may introduce biases. In response, we present a new task of discovering predictive imaging biomarkers directly from the pre-treatment images to learn relevant image features. We propose an evaluation protocol for this task to assess a model's ability to identify predictive imaging biomarkers and differentiate them from prognostic ones. It employs statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally designed for estimating the conditional average treatment effect (CATE) for this task, which previously have been primarily assessed for the precision of CATE estimation, overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates promising results in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. | [
"['Shuhan Xiao' 'Lukas Klein' 'Jens Petersen' 'Philipp Vollmuth'\n 'Paul F. Jaeger' 'Klaus H. Maier-Hein']"
] |
null | null | 2406.02536 | null | null | http://arxiv.org/pdf/2406.02536v1 | 2024-06-04T17:55:38Z | 2024-06-04T17:55:38Z | Mitigate Position Bias in Large Language Models via Scaling a Single
Dimension | Large Language Models (LLMs) are increasingly applied in various real-world scenarios due to their excellent generalization capabilities and robust generative abilities. However, they exhibit position bias, also known as "lost in the middle", a phenomenon that is especially pronounced in long-context scenarios, which indicates the placement of the key information in different positions of a prompt can significantly affect accuracy. This paper first explores the micro-level manifestations of position bias, concluding that attention weights are a micro-level expression of position bias. It further identifies that, in addition to position embeddings, causal attention mask also contributes to position bias by creating position-specific hidden states. Based on these insights, we propose a method to mitigate position bias by scaling this positional hidden states. Experiments on the NaturalQuestions Multi-document QA, KV retrieval, LongBench and timeline reorder tasks, using various models including RoPE models, context windowextended models, and Alibi models, demonstrate the effectiveness and generalizability of our approach. Our method can improve performance by up to 15.2% by modifying just one dimension of hidden states. Our code is available at https://aka.ms/PositionalHidden. | [
"['Yijiong Yu' 'Huiqiang Jiang' 'Xufang Luo' 'Qianhui Wu' 'Chin-Yew Lin'\n 'Dongsheng Li' 'Yuqing Yang' 'Yongfeng Huang' 'Lili Qiu']"
] |
null | null | 2406.02537 | null | null | http://arxiv.org/pdf/2406.02537v1 | 2024-06-04T17:55:43Z | 2024-06-04T17:55:43Z | TopViewRS: Vision-Language Models as Top-View Spatial Reasoners | Top-view perspective denotes a typical way in which humans read and reason over different types of maps, and it is vital for localization and navigation of humans as well as of `non-human' agents, such as the ones backed by large Vision-Language Models (VLMs). Nonetheless, spatial reasoning capabilities of modern VLMs remain unattested and underexplored. In this work, we thus study their capability to understand and reason over spatial relations from the top view. The focus on top view also enables controlled evaluations at different granularity of spatial reasoning; we clearly disentangle different abilities (e.g., recognizing particular objects versus understanding their relative positions). We introduce the TopViewRS (Top-View Reasoning in Space) dataset, consisting of 11,384 multiple-choice questions with either realistic or semantic top-view map as visual input. We then use it to study and evaluate VLMs across 4 perception and reasoning tasks with different levels of complexity. Evaluation of 10 representative open- and closed-source VLMs reveals the gap of more than 50% compared to average human performance, and it is even lower than the random baseline in some cases. Although additional experiments show that Chain-of-Thought reasoning can boost model capabilities by 5.82% on average, the overall performance of VLMs remains limited. Our findings underscore the critical need for enhanced model capability in top-view spatial reasoning and set a foundation for further research towards human-level proficiency of VLMs in real-world multimodal tasks. | [
"['Chengzu Li' 'Caiqi Zhang' 'Han Zhou' 'Nigel Collier' 'Anna Korhonen'\n 'Ivan Vulić']"
] |
null | null | 2406.02539 | null | null | http://arxiv.org/pdf/2406.02539v1 | 2024-06-04T17:56:28Z | 2024-06-04T17:56:28Z | Parrot: Multilingual Visual Instruction Tuning | The rapid development of Multimodal Large Language Models (MLLMs) like GPT-4V has marked a significant step towards artificial general intelligence. Existing methods mainly focus on aligning vision encoders with LLMs through supervised fine-tuning (SFT) to endow LLMs with multimodal abilities, making MLLMs' inherent ability to react to multiple languages progressively deteriorate as the training process evolves. We empirically find that the imbalanced SFT datasets, primarily composed of English-centric image-text pairs, lead to significantly reduced performance in non-English languages. This is due to the failure of aligning the vision encoder and LLM with multilingual tokens during the SFT process. In this paper, we introduce Parrot, a novel method that utilizes textual guidance to drive visual token alignment at the language level. Parrot makes the visual tokens condition on diverse language inputs and uses Mixture-of-Experts (MoE) to promote the alignment of multilingual tokens. Specifically, to enhance non-English visual tokens alignment, we compute the cross-attention using the initial visual features and textual embeddings, the result of which is then fed into the MoE router to select the most relevant experts. The selected experts subsequently convert the initial visual tokens into language-specific visual tokens. Moreover, considering the current lack of benchmarks for evaluating multilingual capabilities within the field, we collect and make available a Massive Multilingual Multimodal Benchmark which includes 6 languages, 15 categories, and 12,000 questions, named as MMMB. Our method not only demonstrates state-of-the-art performance on multilingual MMBench and MMMB, but also excels across a broad range of multimodal tasks. Both the source code and the training dataset of Parrot will be made publicly available. | [
"['Hai-Long Sun' 'Da-Wei Zhou' 'Yang Li' 'Shiyin Lu' 'Chao Yi'\n 'Qing-Guo Chen' 'Zhao Xu' 'Weihua Luo' 'Kaifu Zhang' 'De-Chuan Zhan'\n 'Han-Jia Ye']"
] |
null | null | 2406.02542 | null | null | http://arxiv.org/pdf/2406.02542v1 | 2024-06-04T17:58:03Z | 2024-06-04T17:58:03Z | Loki: Low-Rank Keys for Efficient Sparse Attention | Inference on large language models can be expensive in terms of the compute and memory costs involved, especially when long sequence lengths are used. In particular, the self-attention mechanism used in such models contributes significantly to these costs, which has resulted in several recent works that propose sparse attention approximations for inference. In this work, we propose to approximate the self-attention computation by focusing on the dimensionality of key vectors computed in the attention block. Our analysis reveals that the key vectors lie in a significantly lower-dimensional space, consistently across several datasets and models. Exploiting this observation, we propose Loki, a novel sparse attention method that ranks and selects tokens in the KV-cache based on attention scores computed in low-dimensional space. Our evaluations show that Loki is able to maintain the efficacy of the models better than other popular approximation methods, while speeding up the attention computation due to reduced data movement (load/store) and compute costs. | [
"['Prajwal Singhania' 'Siddharth Singh' 'Shwai He' 'Soheil Feizi'\n 'Abhinav Bhatele']"
] |
null | null | 2406.02543 | null | null | http://arxiv.org/pdf/2406.02543v1 | 2024-06-04T17:58:18Z | 2024-06-04T17:58:18Z | To Believe or Not to Believe Your LLM | We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers). In particular, we derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large, in which case the output of the model is unreliable. This condition can be computed based solely on the output of the model obtained simply by some special iterative prompting based on the previous responses. Such quantification, for instance, allows to detect hallucinations (cases when epistemic uncertainty is high) in both single- and multi-answer responses. This is in contrast to many standard uncertainty quantification strategies (such as thresholding the log-likelihood of a response) where hallucinations in the multi-answer case cannot be detected. We conduct a series of experiments which demonstrate the advantage of our formulation. Further, our investigations shed some light on how the probabilities assigned to a given output by an LLM can be amplified by iterative prompting, which might be of independent interest. | [
"['Yasin Abbasi Yadkori' 'Ilja Kuzborskij' 'András György'\n 'Csaba Szepesvári']"
] |
null | null | 2406.02545 | null | null | http://arxiv.org/pdf/2406.02545v1 | 2024-06-04T17:58:33Z | 2024-06-04T17:58:33Z | Robust and highly scalable estimation of directional couplings from
time-shifted signals | The estimation of directed couplings between the nodes of a network from indirect measurements is a central methodological challenge in scientific fields such as neuroscience, systems biology and economics. Unfortunately, the problem is generally ill-posed due to the possible presence of unknown delays in the measurements. In this paper, we offer a solution of this problem by using a variational Bayes framework, where the uncertainty over the delays is marginalized in order to obtain conservative coupling estimates. To overcome the well-known overconfidence of classical variational methods, we use a hybrid-VI scheme where the (possibly flat or multimodal) posterior over the measurement parameters is estimated using a forward KL loss while the (nearly convex) conditional posterior over the couplings is estimated using the highly scalable gradient-based VI. In our ground-truth experiments, we show that the network provides reliable and conservative estimates of the couplings, greatly outperforming similar methods such as regression DCM. | [
"['Luca Ambrogioni' 'Louis Rouillard' 'Demian Wassermann']"
] |
null | null | 2406.02550 | null | null | http://arxiv.org/pdf/2406.02550v1 | 2024-06-04T17:59:36Z | 2024-06-04T17:59:36Z | Learning to grok: Emergence of in-context learning and skill composition
in modular arithmetic tasks | Large language models can solve tasks that were not present in the training set. This capability is believed to be due to in-context learning and skill composition. In this work, we study the emergence of in-context learning and skill composition in a collection of modular arithmetic tasks. Specifically, we consider a finite collection of linear modular functions $z = a , x + b , y ;mathrm{mod}; p$ labeled by the vector $(a, b) in mathbb{Z}_p^2$. We use some of these tasks for pre-training and the rest for out-of-distribution testing. We empirically show that a GPT-style transformer exhibits a transition from in-distribution to out-of-distribution generalization as the number of pre-training tasks increases. We find that the smallest model capable of out-of-distribution generalization requires two transformer blocks, while for deeper models, the out-of-distribution generalization phase is emph{transient}, necessitating early stopping. Finally, we perform an interpretability study of the pre-trained models, revealing the highly structured representations in both phases; and discuss the learnt algorithm. | [
"['Tianyu He' 'Darshil Doshi' 'Aritra Das' 'Andrey Gromov']"
] |
null | null | 2406.02554 | null | null | http://arxiv.org/pdf/2406.02554v1 | 2024-03-22T22:52:35Z | 2024-03-22T22:52:35Z | Hear Me, See Me, Understand Me: Audio-Visual Autism Behavior Recognition | In this article, we introduce a novel problem of audio-visual autism behavior recognition, which includes social behavior recognition, an essential aspect previously omitted in AI-assisted autism screening research. We define the task at hand as one that is audio-visual autism behavior recognition, which uses audio and visual cues, including any speech present in the audio, to recognize autism-related behaviors. To facilitate this new research direction, we collected an audio-visual autism spectrum dataset (AV-ASD), currently the largest video dataset for autism screening using a behavioral approach. It covers an extensive range of autism-associated behaviors, including those related to social communication and interaction. To pave the way for further research on this new problem, we intensively explored leveraging foundation models and multimodal large language models across different modalities. Our experiments on the AV-ASD dataset demonstrate that integrating audio, visual, and speech modalities significantly enhances the performance in autism behavior recognition. Additionally, we explored the use of a post-hoc to ad-hoc pipeline in a multimodal large language model to investigate its potential to augment the model's explanatory capability during autism behavior recognition. We will release our dataset, code, and pre-trained models. | [
"['Shijian Deng' 'Erin E. Kosloski' 'Siddhi Patel' 'Zeke A. Barnett'\n 'Yiyang Nan' 'Alexander Kaplan' 'Sisira Aarukapalli' 'William T. Doan'\n 'Matthew Wang' 'Harsh Singh' 'Pamela R. Rollins' 'Yapeng Tian']"
] |
null | null | 2406.02560 | null | null | http://arxiv.org/pdf/2406.02560v2 | 2024-06-15T22:02:03Z | 2024-04-22T17:40:08Z | Less Peaky and More Accurate CTC Forced Alignment by Label Priors | Connectionist temporal classification (CTC) models are known to have peaky output distributions. Such behavior is not a problem for automatic speech recognition (ASR), but it can cause inaccurate forced alignments (FA), especially at finer granularity, e.g., phoneme level. This paper aims at alleviating the peaky behavior for CTC and improve its suitability for forced alignment generation, by leveraging label priors, so that the scores of alignment paths containing fewer blanks are boosted and maximized during training. As a result, our CTC model produces less peaky posteriors and is able to more accurately predict the offset of the tokens besides their onset. It outperforms the standard CTC model and a heuristics-based approach for obtaining CTC's token offset timestamps by 12-40% in phoneme and word boundary errors (PBE and WBE) measured on the Buckeye and TIMIT data. Compared with the most widely used FA toolkit Montreal Forced Aligner (MFA), our method performs similarly on PBE/WBE on Buckeye, yet falls behind MFA on TIMIT. Nevertheless, our method has a much simpler training pipeline and better runtime efficiency. Our training recipe and pretrained model are released in TorchAudio. | [
"['Ruizhe Huang' 'Xiaohui Zhang' 'Zhaoheng Ni' 'Li Sun' 'Moto Hira'\n 'Jeff Hwang' 'Vimal Manohar' 'Vineel Pratap' 'Matthew Wiesner'\n 'Shinji Watanabe' 'Daniel Povey' 'Sanjeev Khudanpur']"
] |
null | null | 2406.02566 | null | null | http://arxiv.org/pdf/2406.02566v1 | 2024-05-03T19:24:41Z | 2024-05-03T19:24:41Z | Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active
Learning Pipeline for Speech Recognition | Emphasizing a data-centric AI approach, this paper introduces a novel two-stage active learning (AL) pipeline for automatic speech recognition (ASR), combining unsupervised and supervised AL methods. The first stage utilizes unsupervised AL by using x-vectors clustering for diverse sample selection from unlabeled speech data, thus establishing a robust initial dataset for the subsequent supervised AL. The second stage incorporates a supervised AL strategy, with a batch AL method specifically developed for ASR, aimed at selecting diverse and informative batches of samples. Here, sample diversity is also achieved using x-vectors clustering, while the most informative samples are identified using a Bayesian AL method tailored for ASR with an adaptation of Monte Carlo dropout to approximate Bayesian inference. This approach enables precise uncertainty estimation, thereby enhancing ASR model training with significantly reduced data requirements. Our method has shown superior performance compared to competing methods on homogeneous, heterogeneous, and OOD test sets, demonstrating that strategic sample selection and innovative Bayesian modeling can substantially optimize both labeling effort and data utilization in deep learning-based ASR applications. | [
"['Ognjen Kundacina' 'Vladimir Vincan' 'Dragisa Miskovic']"
] |
null | null | 2406.02572 | null | null | http://arxiv.org/pdf/2406.02572v1 | 2024-05-16T07:12:47Z | 2024-05-16T07:12:47Z | Selfsupervised learning for pathological speech detection | Speech production is a complex phenomenon, wherein the brain orchestrates a sequence of processes involving thought processing, motor planning, and the execution of articulatory movements. However, this intricate execution of various processes is susceptible to influence and disruption by various neurodegenerative pathological speech disorders, such as Parkinsons' disease, resulting in dysarthria, apraxia, and other conditions. These disorders lead to pathological speech characterized by abnormal speech patterns and imprecise articulation. Diagnosing these speech disorders in clinical settings typically involves auditory perceptual tests, which are time-consuming, and the diagnosis can vary among clinicians based on their experiences, biases, and cognitive load during the diagnosis. Additionally, unlike neurotypical speakers, patients with speech pathologies or impairments are unable to access various virtual assistants such as Alexa, Siri, etc. To address these challenges, several automatic pathological speech detection (PSD) approaches have been proposed. These approaches aim to provide efficient and accurate detection of speech disorders, thereby facilitating timely intervention and support for individuals affected by these conditions. These approaches mainly vary in two aspects: the input representations utilized and the classifiers employed. Due to the limited availability of data, the performance of detection remains subpar. Self-supervised learning (SSL) embeddings, such as wav2vec2, and their multilingual versions, are being explored as a promising avenue to improve performance. These embeddings leverage self-supervised learning techniques to extract rich representations from audio data, thereby offering a potential solution to address the limitations posed by the scarcity of labeled data. | [
"['Shakeel Ahmad Sheikh']"
] |
null | null | 2406.02575 | null | null | http://arxiv.org/pdf/2406.02575v1 | 2024-05-27T20:29:13Z | 2024-05-27T20:29:13Z | Cross-Modal Safety Alignment: Is textual unlearning all you need? | Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervised Fine-tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF). While further SFT and RLHF-based safety training can be conducted in multi-modal settings, collecting multi-modal training datasets poses a significant challenge. Inspired by the structural design of recent multi-modal models, where, regardless of the combination of input modalities, all inputs are ultimately fused into the language space, we aim to explore whether unlearning solely in the textual domain can be effective for cross-modality safety alignment. Our evaluation across six datasets empirically demonstrates the transferability -- textual unlearning in VLMs significantly reduces the Attack Success Rate (ASR) to less than 8% and in some cases, even as low as nearly 2% for both text-based and vision-text-based attacks, alongside preserving the utility. Moreover, our experiments show that unlearning with a multi-modal dataset offers no potential benefits but incurs significantly increased computational demands, possibly up to 6 times higher. | [
"['Trishna Chakraborty' 'Erfan Shayegani' 'Zikui Cai' 'Nael Abu-Ghazaleh'\n 'M. Salman Asif' 'Yue Dong' 'Amit K. Roy-Chowdhury' 'Chengyu Song']"
] |
null | null | 2406.02577 | null | null | http://arxiv.org/pdf/2406.02577v1 | 2024-05-28T23:28:28Z | 2024-05-28T23:28:28Z | Are PPO-ed Language Models Hackable? | Numerous algorithms have been proposed to $textit{align}$ language models to remove undesirable behaviors. However, the challenges associated with a very large state space and creating a proper reward function often result in various jailbreaks. Our paper aims to examine this effect of reward in the controlled setting of positive sentiment language generation. Instead of online training of a reward model based on human feedback, we employ a statically learned sentiment classifier. We also consider a setting where our model's weights and activations are exposed to an end-user after training. We examine a pretrained GPT-2 through the lens of mechanistic interpretability before and after proximal policy optimization (PPO) has been applied to promote positive sentiment responses. Using these insights, we (1) attempt to "hack" the PPO-ed model to generate negative sentiment responses and (2) add a term to the reward function to try and alter `negative' weights. | [
"['Suraj Anand' 'David Getzen']"
] |
null | null | 2406.02578 | null | null | http://arxiv.org/pdf/2406.02578v1 | 2024-05-29T00:07:22Z | 2024-05-29T00:07:22Z | Pretrained Mobility Transformer: A Foundation Model for Human Mobility | Ubiquitous mobile devices are generating vast amounts of location-based service data that reveal how individuals navigate and utilize urban spaces in detail. In this study, we utilize these extensive, unlabeled sequences of user trajectories to develop a foundation model for understanding urban space and human mobility. We introduce the textbf{P}retrained textbf{M}obility textbf{T}ransformer (PMT), which leverages the transformer architecture to process user trajectories in an autoregressive manner, converting geographical areas into tokens and embedding spatial and temporal information within these representations. Experiments conducted in three U.S. metropolitan areas over a two-month period demonstrate PMT's ability to capture underlying geographic and socio-demographic characteristics of regions. The proposed PMT excels across various downstream tasks, including next-location prediction, trajectory imputation, and trajectory generation. These results support PMT's capability and effectiveness in decoding complex patterns of human mobility, offering new insights into urban spatial functionality and individual mobility preferences. | [
"['Xinhua Wu' 'Haoyu He' 'Yanchao Wang' 'Qi Wang']"
] |
null | null | 2406.02579 | null | null | http://arxiv.org/abs/2406.02579v1 | 2024-05-29T10:10:53Z | 2024-05-29T10:10:53Z | An Open-Source Framework for Efficient Numerically-Tailored Computations | We present a versatile open-source framework designed to facilitate efficient, numerically-tailored Matrix-Matrix Multiplications (MMMs). The framework offers two primary contributions: first, a fine-tuned, automated pipeline for arithmetic datapath generation, enabling highly customizable systolic MMM kernels; second, seamless integration of the generated kernels into user code, irrespective of the programming language employed, without necessitating modifications. The framework demonstrates a systematic enhancement in accuracy per energy cost across diverse High Performance Computing (HPC) workloads displaying a variety of numerical requirements, such as Artificial Intelligence (AI) inference and Sea Surface Height (SSH) computation. For AI inference, we consider a set of state-of-the-art neural network models, namely ResNet18, ResNet34, ResNet50, DenseNet121, DenseNet161, DenseNet169, and VGG11, in conjunction with two datasets, two computer formats, and 27 distinct intermediate arithmetic datapaths. Our approach consistently reduces energy consumption across all cases, with a notable example being the reduction by factors of $3.3times$ for IEEE754-32 and $1.4times$ for Bfloat16 during ImageNet inference with ResNet50. This is accomplished while maintaining accuracies of $82.3%$ and $86%$, comparable to those achieved with conventional Floating-Point Units (FPUs). In the context of SSH computation, our method achieves fully-reproducible results using double-precision words, surpassing the accuracy of conventional double- and quad-precision arithmetic in FPUs. Our approach enhances SSH computation accuracy by a minimum of $5times$ and $27times$ compared to IEEE754-64 and IEEE754-128, respectively, resulting in $5.6times$ and $15.1times$ improvements in accuracy per power cost. | [
"['Louis Ledoux' 'Marc Casas']"
] |
null | null | 2406.02580 | null | null | http://arxiv.org/pdf/2406.02580v1 | 2024-05-29T22:03:23Z | 2024-05-29T22:03:23Z | Exploiting Chaotic Dynamics as Deep Neural Networks | Chaos presents complex dynamics arising from nonlinearity and a sensitivity to initial states. These characteristics suggest a depth of expressivity that underscores their potential for advanced computational applications. However, strategies to effectively exploit chaotic dynamics for information processing have largely remained elusive. In this study, we reveal that the essence of chaos can be found in various state-of-the-art deep neural networks. Drawing inspiration from this revelation, we propose a novel method that directly leverages chaotic dynamics for deep learning architectures. Our approach is systematically evaluated across distinct chaotic systems. In all instances, our framework presents superior results to conventional deep neural networks in terms of accuracy, convergence speed, and efficiency. Furthermore, we found an active role of transient chaos formation in our scheme. Collectively, this study offers a new path for the integration of chaos, which has long been overlooked in information processing, and provides insights into the prospective fusion of chaotic dynamics within the domains of machine learning and neuromorphic computation. | [
"['Shuhong Liu' 'Nozomi Akashi' 'Qingyao Huang' 'Yasuo Kuniyoshi'\n 'Kohei Nakajima']"
] |
null | null | 2406.02581 | null | null | http://arxiv.org/pdf/2406.02581v1 | 2024-05-30T01:55:44Z | 2024-05-30T01:55:44Z | Constrained or Unconstrained? Neural-Network-Based Equation Discovery
from Data | Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE by solving a constrained optimization problem and using an intermediate state representation similar to a Physics-Informed Neural Network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the PDE is satisfied at several spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on the Burgers' and the Korteweg-De Vreis equations demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. For both methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. We briefly highlight other small, yet crucial, implementation details. | [
"['Grant Norman' 'Jacqueline Wentz' 'Hemanth Kolla' 'Kurt Maute'\n 'Alireza Doostan']"
] |
null | null | 2406.02582 | null | null | http://arxiv.org/pdf/2406.02582v1 | 2024-05-30T19:18:20Z | 2024-05-30T19:18:20Z | Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning | Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet that was inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even for cases when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period. | [
"['Yinan Wang' 'M. Giselle Fernández-Godino' 'Nipun Gunawardena'\n 'Donald D. Lucas' 'Xiaowei Yue']"
] |
null | null | 2406.02583 | null | null | http://arxiv.org/pdf/2406.02583v1 | 2024-05-30T20:40:16Z | 2024-05-30T20:40:16Z | Exploring the Potential of Polynomial Basis Functions in
Kolmogorov-Arnold Networks: A Comparative Study of Different Groups of
Polynomials | This paper presents a comprehensive survey of 18 distinct polynomials and their potential applications in Kolmogorov-Arnold Network (KAN) models as an alternative to traditional spline-based methods. The polynomials are classified into various groups based on their mathematical properties, such as orthogonal polynomials, hypergeometric polynomials, q-polynomials, Fibonacci-related polynomials, combinatorial polynomials, and number-theoretic polynomials. The study aims to investigate the suitability of these polynomials as basis functions in KAN models for complex tasks like handwritten digit classification on the MNIST dataset. The performance metrics of the KAN models, including overall accuracy, Kappa, and F1 score, are evaluated and compared. The Gottlieb-KAN model achieves the highest performance across all metrics, suggesting its potential as a suitable choice for the given task. However, further analysis and tuning of these polynomials on more complex datasets are necessary to fully understand their capabilities in KAN models. The source code for the implementation of these KAN models is available at https://github.com/seydi1370/Basis_Functions . | [
"['Seyd Teymoor Seydi']"
] |
null | null | 2406.02584 | null | null | http://arxiv.org/pdf/2406.02584v2 | 2024-07-05T15:37:15Z | 2024-05-30T20:48:10Z | Planetary Causal Inference: Implications for the Geography of Poverty | Earth observation data such as satellite imagery can, when combined with machine learning, can have far-reaching impacts on our understanding of the geography of poverty through the prediction of living conditions, especially where government-derived economic indicators are either unavailable or potentially untrustworthy. Recent work has progressed in using Earth Observation (EO) data not only to predict spatial economic outcomes but also to explore cause and effect, an understanding which is critical for downstream policy analysis. In this review, we first document the growth of interest in using satellite images together with EO data in causal analysis. We then trace the relationship between spatial statistics and machine learning methods before discussing four ways in which EO data has been used in causal machine learning pipelines -- (1.) poverty outcome imputation for downstream causal analysis, (2.) EO image deconfounding, (3.) EO-based treatment effect heterogeneity, and (4.) EO-based transportability analysis. We conclude by providing a step-by-step workflow for how researchers can incorporate EO data in causal ML analysis going forward, outlining major choices of data, models, and evaluation metrics. | [
"['Kazuki Sakamoto' 'Connor T. Jerzak' 'Adel Daoud']"
] |
null | null | 2406.02585 | null | null | http://arxiv.org/pdf/2406.02585v1 | 2024-05-30T20:52:23Z | 2024-05-30T20:52:23Z | Contextual Counting: A Mechanistic Study of Transformers on a
Quantitative Task | Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel toy problem aimed at enhancing our understanding of Transformers in quantitative and scientific contexts. This task requires precise localization and computation within datasets, akin to object detection or region-based scientific analysis. We present theoretical and empirical analysis using both causal and non-causal Transformer architectures, investigating the influence of various positional encodings on performance and interpretability. In particular, we find that causal attention is much better suited for the task, and that no positional embeddings lead to the best accuracy, though rotary embeddings are competitive and easier to train. We also show that out of distribution performance is tightly linked to which tokens it uses as a bias term. | [
"['Siavash Golkar' 'Alberto Bietti' 'Mariel Pettee' 'Michael Eickenberg'\n 'Miles Cranmer' 'Keiya Hirashima' 'Geraud Krawezik' 'Nicholas Lourie'\n 'Michael McCabe' 'Rudy Morel' 'Ruben Ohana' 'Liam Holden Parker'\n 'Bruno Régaldo-Saint Blancard' 'Kyunghyun Cho' 'Shirley Ho']"
] |
null | null | 2406.02587 | null | null | http://arxiv.org/pdf/2406.02587v1 | 2024-05-31T03:04:10Z | 2024-05-31T03:04:10Z | Capturing Climatic Variability: Using Deep Learning for Stochastic
Downscaling | Adapting to the changing climate requires accurate local climate information, a computationally challenging problem. Recent studies have used Generative Adversarial Networks (GANs), a type of deep learning, to learn complex distributions and downscale climate variables efficiently. Capturing variability while downscaling is crucial for estimating uncertainty and characterising extreme events - critical information for climate adaptation. Since downscaling is an undetermined problem, many fine-scale states are physically consistent with the coarse-resolution state. To quantify this ill-posed problem, downscaling techniques should be stochastic, able to sample realisations from a high-resolution distribution conditioned on low-resolution input. Previous stochastic downscaling attempts have found substantial underdispersion, with models failing to represent the full distribution. We propose approaches to improve the stochastic calibration of GANs in three ways: a) injecting noise inside the network, b) adjusting the training process to explicitly account for the stochasticity, and c) using a probabilistic loss metric. We tested our models first on a synthetic dataset with known distributional properties, and then on a realistic downscaling scenario, predicting high-resolution wind components from low-resolution climate covariates. Injecting noise, on its own, substantially improved the quality of conditional and full distributions in tests with synthetic data, but performed less well for wind field downscaling, where models remained underdispersed. For wind downscaling, we found that adjusting the training method and including the probabilistic loss improved calibration. The best model, with all three changes, showed much improved skill at capturing the full variability of the high-resolution distribution and thus at characterising extremes. | [
"['Kiri Daust' 'Adam Monahan']"
] |
null | null | 2406.02591 | null | null | http://arxiv.org/pdf/2406.02591v1 | 2024-05-31T19:16:07Z | 2024-05-31T19:16:07Z | Unveiling the Potential of AI for Nanomaterial Morphology Prediction | Creation of nanomaterials with specific morphology remains a complex experimental process, even though there is a growing demand for these materials in various industry sectors. This study explores the potential of AI to predict the morphology of nanoparticles within the data availability constraints. For that, we first generated a new multi-modal dataset that is double the size of analogous studies. Then, we systematically evaluated performance of classical machine learning and large language models in prediction of nanomaterial shapes and sizes. Finally, we prototyped a text-to-image system, discussed the obtained empirical results, as well as the limitations and promises of existing approaches. | [
"['Ivan Dubrovsky' 'Andrei Dmitrenko' 'Aleksei Dmitrenko' 'Nikita Serov'\n 'Vladimir Vinogradov']"
] |
null | null | 2406.02592 | null | null | http://arxiv.org/pdf/2406.02592v1 | 2024-05-31T21:18:25Z | 2024-05-31T21:18:25Z | LOLAMEME: Logic, Language, Memory, Mechanistic Framework | The performance of Large Language Models has achieved superhuman breadth with unprecedented depth. At the same time, the language models are mostly black box models and the underlying mechanisms for performance have been evaluated using synthetic or mechanistic schemes. We extend current mechanistic schemes to incorporate Logic, memory, and nuances of Language such as latent structure. The proposed framework is called LOLAMEME and we provide two instantiations of LOLAMEME: LoLa and MeMe languages. We then consider two generative language model architectures: transformer-based GPT-2 and convolution-based Hyena. We propose the hybrid architecture T HEX and use LOLAMEME framework is used to compare three architectures. T HEX outperforms GPT-2 and Hyena on select tasks. | [
"['Jay Desai' 'Xiaobo Guo' 'Srinivasan H. Sengamedu']"
] |
null | null | 2406.02594 | null | null | http://arxiv.org/pdf/2406.02594v1 | 2024-06-01T02:47:39Z | 2024-06-01T02:47:39Z | Graph Neural Networks for Brain Graph Learning: A Survey | Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at url{https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs}. | [
"['Xuexiong Luo' 'Jia Wu' 'Jian Yang' 'Shan Xue' 'Amin Beheshti'\n 'Quan Z. Sheng' 'David McAlpine' 'Paul Sowman' 'Alexis Giral'\n 'Philip S. Yu']"
] |
null | null | 2406.02596 | null | null | http://arxiv.org/pdf/2406.02596v1 | 2024-06-01T05:55:15Z | 2024-06-01T05:55:15Z | Slow and Steady Wins the Race: Maintaining Plasticity with Hare and
Tortoise Networks | This study investigates the loss of generalization ability in neural networks, revisiting warm-starting experiments from Ash & Adams. Our empirical analysis reveals that common methods designed to enhance plasticity by maintaining trainability provide limited benefits to generalization. While reinitializing the network can be effective, it also risks losing valuable prior knowledge. To this end, we introduce the Hare & Tortoise, inspired by the brain's complementary learning system. Hare & Tortoise consists of two components: the Hare network, which rapidly adapts to new information analogously to the hippocampus, and the Tortoise network, which gradually integrates knowledge akin to the neocortex. By periodically reinitializing the Hare network to the Tortoise's weights, our method preserves plasticity while retaining general knowledge. Hare & Tortoise can effectively maintain the network's ability to generalize, which improves advanced reinforcement learning algorithms on the Atari-100k benchmark. The code is available at https://github.com/dojeon-ai/hare-tortoise. | [
"['Hojoon Lee' 'Hyeonseo Cho' 'Hyunseung Kim' 'Donghu Kim' 'Dugki Min'\n 'Jaegul Choo' 'Clare Lyle']"
] |
null | null | 2406.02597 | null | null | http://arxiv.org/pdf/2406.02597v1 | 2024-06-01T14:32:19Z | 2024-06-01T14:32:19Z | CoNO: Complex Neural Operator for Continous Dynamical Physical Systems | Neural operators extend data-driven models to map between infinite-dimensional functional spaces. While these operators perform effectively in either the time or frequency domain, their performance may be limited when applied to non-stationary spatial or temporal signals whose frequency characteristics change with time. Here, we introduce Complex Neural Operator (CoNO) that parameterizes the integral kernel using Fractional Fourier Transform (FrFT), better representing non-stationary signals in a complex-valued domain. Theoretically, we prove the universal approximation capability of CoNO. We perform an extensive empirical evaluation of CoNO on seven challenging partial differential equations (PDEs), including regular grids, structured meshes, and point clouds. Empirically, CoNO consistently attains state-of-the-art performance, showcasing an average relative gain of 10.9%. Further, CoNO exhibits superior performance, outperforming all other models in additional tasks such as zero-shot super-resolution and robustness to noise. CoNO also exhibits the ability to learn from small amounts of data -- giving the same performance as the next best model with just 60% of the training data. Altogether, CoNO presents a robust and superior model for modeling continuous dynamical systems, providing a fillip to scientific machine learning. | [
"['Karn Tiwari' 'N M Anoop Krishnan' 'A P Prathosh']"
] |
null | null | 2406.02598 | null | null | http://arxiv.org/pdf/2406.02598v1 | 2024-06-01T16:18:20Z | 2024-06-01T16:18:20Z | Towards Learning Foundation Models for Heuristic Functions to Solve
Pathfinding Problems | Pathfinding problems are found throughout robotics, computational science, and natural sciences. Traditional methods to solve these require training deep neural networks (DNNs) for each new problem domain, consuming substantial time and resources. This study introduces a novel foundation model, leveraging deep reinforcement learning to train heuristic functions that seamlessly adapt to new domains without further fine-tuning. Building upon DeepCubeA, we enhance the model by providing the heuristic function with the domain's state transition information, improving its adaptability. Utilizing a puzzle generator for the 15-puzzle action space variation domains, we demonstrate our model's ability to generalize and solve unseen domains. We achieve a strong correlation between learned and ground truth heuristic values across various domains, as evidenced by robust R-squared and Concordance Correlation Coefficient metrics. These results underscore the potential of foundation models to establish new standards in efficiency and adaptability for AI-driven solutions in complex pathfinding problems. | [
"['Vedant Khandelwal' 'Amit Sheth' 'Forest Agostinelli']"
] |
null | null | 2406.02600 | null | null | http://arxiv.org/pdf/2406.02600v1 | 2024-06-01T23:07:05Z | 2024-06-01T23:07:05Z | Data Quality in Edge Machine Learning: A State-of-the-Art Survey | Data-driven Artificial Intelligence (AI) systems trained using Machine Learning (ML) are shaping an ever-increasing (in size and importance) portion of our lives, including, but not limited to, recommendation systems, autonomous driving technologies, healthcare diagnostics, financial services, and personalized marketing. On the one hand, the outsized influence of these systems imposes a high standard of quality, particularly in the data used to train them. On the other hand, establishing and maintaining standards of Data Quality (DQ) becomes more challenging due to the proliferation of Edge Computing and Internet of Things devices, along with their increasing adoption for training and deploying ML models. The nature of the edge environment -- characterized by limited resources, decentralized data storage, and processing -- exacerbates data-related issues, making them more frequent, severe, and difficult to detect and mitigate. From these observations, it follows that DQ research for edge ML is a critical and urgent exploration track for the safety and robust usefulness of present and future AI systems. Despite this fact, DQ research for edge ML is still in its infancy. The literature on this subject remains fragmented and scattered across different research communities, with no comprehensive survey to date. Hence, this paper aims to fill this gap by providing a global view of the existing literature from multiple disciplines that can be grouped under the umbrella of DQ for edge ML. Specifically, we present a tentative definition of data quality in Edge computing, which we use to establish a set of DQ dimensions. We explore each dimension in detail, including existing solutions for mitigation. | [
"['Mohammed Djameleddine Belgoumri' 'Mohamed Reda Bouadjenek' 'Sunil Aryal'\n 'Hakim Hacid']"
] |
null | null | 2406.02601 | null | null | http://arxiv.org/pdf/2406.02601v1 | 2024-06-02T01:13:01Z | 2024-06-02T01:13:01Z | Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding
Alignment Approach for Healthcare Applications | Large-scale multi-modal deep learning models have revolutionized domains such as healthcare, highlighting the importance of computational power. However, in resource-constrained regions like Low and Middle-Income Countries (LMICs), limited access to GPUs and data poses significant challenges, often leaving CPUs as the sole resource. To address this, we advocate for leveraging vector embeddings to enable flexible and efficient computational methodologies, democratizing multimodal deep learning across diverse contexts. Our paper investigates the efficiency and effectiveness of using vector embeddings from single-modal foundation models and multi-modal Vision-Language Models (VLMs) for multimodal deep learning in low-resource environments, particularly in healthcare. Additionally, we propose a simple yet effective inference-time method to enhance performance by aligning image-text embeddings. Comparing these approaches with traditional methods, we assess their impact on computational efficiency and model performance using metrics like accuracy, F1-score, inference time, training time, and memory usage across three medical modalities: BRSET (ophthalmology), HAM10000 (dermatology), and SatelliteBench (public health). Our findings show that embeddings reduce computational demands without compromising model performance. Furthermore, our alignment method improves performance in medical tasks. This research promotes sustainable AI practices by optimizing resources in constrained environments, highlighting the potential of embedding-based approaches for efficient multimodal learning. Vector embeddings democratize multimodal deep learning in LMICs, particularly in healthcare, enhancing AI adaptability in varied use cases. | [
"['David Restrepo' 'Chenwei Wu' 'Sebastián Andrés Cajas'\n 'Luis Filipe Nakayama' 'Leo Anthony Celi' 'Diego M López']"
] |
null | null | 2406.02602 | null | null | http://arxiv.org/abs/2406.02602v1 | 2024-06-02T02:33:14Z | 2024-06-02T02:33:14Z | D-FaST: Cognitive Signal Decoding with Disentangled
Frequency-Spatial-Temporal Attention | Cognitive Language Processing (CLP), situated at the intersection of Natural Language Processing (NLP) and cognitive science, plays a progressively pivotal role in the domains of artificial intelligence, cognitive intelligence, and brain science. Among the essential areas of investigation in CLP, Cognitive Signal Decoding (CSD) has made remarkable achievements, yet there still exist challenges related to insufficient global dynamic representation capability and deficiencies in multi-domain feature integration. In this paper, we introduce a novel paradigm for CLP referred to as Disentangled Frequency-Spatial-Temporal Attention(D-FaST). Specifically, we present an novel cognitive signal decoder that operates on disentangled frequency-space-time domain attention. This decoder encompasses three key components: frequency domain feature extraction employing multi-view attention, spatial domain feature extraction utilizing dynamic brain connection graph attention, and temporal feature extraction relying on local time sliding window attention. These components are integrated within a novel disentangled framework. Additionally, to encourage advancements in this field, we have created a new CLP dataset, MNRED. Subsequently, we conducted an extensive series of experiments, evaluating D-FaST's performance on MNRED, as well as on publicly available datasets including ZuCo, BCIC IV-2A, and BCIC IV-2B. Our experimental results demonstrate that D-FaST outperforms existing methods significantly on both our datasets and traditional CSD datasets including establishing a state-of-the-art accuracy score 78.72% on MNRED, pushing the accuracy score on ZuCo to 78.35%, accuracy score on BCIC IV-2A to 74.85% and accuracy score on BCIC IV-2B to 76.81%. | [
"['Weiguo Chen' 'Changjian Wang' 'Kele Xu' 'Yuan Yuan' 'Yanru Bai'\n 'Dongsong Zhang']"
] |
null | null | 2406.02603 | null | null | http://arxiv.org/pdf/2406.02603v1 | 2024-06-02T04:07:32Z | 2024-06-02T04:07:32Z | Distortion-free Watermarks are not Truly Distortion-free under Watermark
Key Collisions | Language model (LM) watermarking techniques inject a statistical signal into LM-generated content by substituting the random sampling process with pseudo-random sampling, using watermark keys as the random seed. Among these statistical watermarking approaches, distortion-free watermarks are particularly crucial because they embed watermarks into LM-generated content without compromising generation quality. However, one notable limitation of pseudo-random sampling compared to true-random sampling is that, under the same watermark keys (i.e., key collision), the results of pseudo-random sampling exhibit correlations. This limitation could potentially undermine the distortion-free property. Our studies reveal that key collisions are inevitable due to the limited availability of watermark keys, and existing distortion-free watermarks exhibit a significant distribution bias toward the original LM distribution in the presence of key collisions. Moreover, achieving a perfect distortion-free watermark is impossible as no statistical signal can be embedded under key collisions. To reduce the distribution bias caused by key collisions, we introduce a new family of distortion-free watermarks--beta-watermark. Experimental results support that the beta-watermark can effectively reduce the distribution bias under key collisions. | [
"['Yihan Wu' 'Ruibo Chen' 'Zhengmian Hu' 'Yanshuo Chen' 'Junfeng Guo'\n 'Hongyang Zhang' 'Heng Huang']"
] |
null | null | 2406.02604 | null | null | http://arxiv.org/pdf/2406.02604v1 | 2024-06-02T06:39:01Z | 2024-06-02T06:39:01Z | Gated recurrent neural network with TPE Bayesian optimization for
enhancing stock index prediction accuracy | The recent advancement of deep learning architectures, neural networks, and the combination of abundant financial data and powerful computers are transforming finance, leading us to develop an advanced method for predicting future stock prices. However, the accessibility of investment and trading at everyone's fingertips made the stock markets increasingly intricate and prone to volatility. The increased complexity and volatility of the stock market have driven demand for more models, which would effectively capture high volatility and non-linear behavior of the different stock prices. This study explored gated recurrent neural network (GRNN) algorithms such as LSTM (long short-term memory), GRU (gated recurrent unit), and hybrid models like GRU-LSTM, LSTM-GRU, with Tree-structured Parzen Estimator (TPE) Bayesian optimization for hyperparameter optimization (TPE-GRNN). The aim is to improve the prediction accuracy of the next day's closing price of the NIFTY 50 index, a prominent Indian stock market index, using TPE-GRNN. A combination of eight influential factors is carefully chosen from fundamental stock data, technical indicators, crude oil price, and macroeconomic data to train the models for capturing the changes in the price of the index with the factors of the broader economy. Single-layer and multi-layer TPE-GRNN models have been developed. The models' performance is evaluated using standard matrices like R2, MAPE, and RMSE. The analysis of models' performance reveals the impact of feature selection and hyperparameter optimization (HPO) in enhancing stock index price prediction accuracy. The results show that the MAPE of our proposed TPE-LSTM method is the lowest (best) with respect to all the previous models for stock index price prediction. | [
"['Bivas Dinda']"
] |
null | null | 2406.02605 | null | null | http://arxiv.org/pdf/2406.02605v1 | 2024-06-02T12:37:12Z | 2024-06-02T12:37:12Z | A Novel Defense Against Poisoning Attacks on Federated Learning:
LayerCAM Augmented with Autoencoder | Recent attacks on federated learning (FL) can introduce malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This paper proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in federated learning. The LayerCAM-AE puts forth a new Layer Class Activation Mapping (LayerCAM) integrated with an autoencoder (AE), significantly enhancing detection capabilities. Specifically, LayerCAM-AE generates a heat map for each local model update, which is then transformed into a more compact visual format. The autoencoder is designed to process the LayerCAM heat maps from the local model updates, improving their distinctiveness and thereby increasing the accuracy in spotting anomalous maps and malicious local models. To address the risk of misclassifications with LayerCAM-AE, a voting algorithm is developed, where a local model update is flagged as malicious if its heat maps are consistently suspicious over several rounds of communication. Extensive tests of LayerCAM-AE on the SVHN and CIFAR-100 datasets are performed under both Independent and Identically Distributed (IID) and non-IID settings in comparison with existing ResNet-50 and REGNETY-800MF defense models. Experimental results show that LayerCAM-AE increases detection rates (Recall: 1.0, Precision: 1.0, FPR: 0.0, Accuracy: 1.0, F1 score: 1.0, AUC: 1.0) and test accuracy in FL, surpassing the performance of both the ResNet-50 and REGNETY-800MF. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE | [
"['Jingjing Zheng' 'Xin Yuan' 'Kai Li' 'Wei Ni' 'Eduardo Tovar'\n 'Jon Crowcroft']"
] |
null | null | 2406.02606 | null | null | http://arxiv.org/pdf/2406.02606v1 | 2024-06-02T18:26:50Z | 2024-06-02T18:26:50Z | Know Your Neighborhood: General and Zero-Shot Capable Binary Function
Search Powered by Call Graphlets | Binary code similarity detection is an important problem with applications in areas like malware analysis, vulnerability research and plagiarism detection. This paper proposes a novel graph neural network architecture combined with a novel graph data representation called call graphlets. A call graphlet encodes the neighborhood around each function in a binary executable, capturing the local and global context through a series of statistical features. A specialized graph neural network model is then designed to operate on this graph representation, learning to map it to a feature vector that encodes semantic code similarities using deep metric learning. The proposed approach is evaluated across four distinct datasets covering different architectures, compiler toolchains, and optimization levels. Experimental results demonstrate that the combination of call graphlets and the novel graph neural network architecture achieves state-of-the-art performance compared to baseline techniques across cross-architecture, mono-architecture and zero shot tasks. In addition, our proposed approach also performs well when evaluated against an out-of-domain function inlining task. Overall, the work provides a general and effective graph neural network-based solution for conducting binary code similarity detection. | [
"['Joshua Collyer' 'Tim Watson' 'Iain Phillips']"
] |
null | null | 2406.02608 | null | null | http://arxiv.org/pdf/2406.02608v1 | 2024-06-03T01:07:42Z | 2024-06-03T01:07:42Z | PPINtonus: Early Detection of Parkinson's Disease Using Deep-Learning
Tonal Analysis | PPINtonus is a system for the early detection of Parkinson's Disease (PD) utilizing deep-learning tonal analysis, providing a cost-effective and accessible alternative to traditional neurological examinations. Partnering with the Parkinson's Voice Project (PVP), PPINtonus employs a semi-supervised conditional generative adversarial network to generate synthetic data points, enhancing the training dataset for a multi-layered deep neural network. Combined with PRAAT phonetics software, this network accurately assesses biomedical voice measurement values from a simple 120-second vocal test performed with a standard microphone in typical household noise conditions. The model's performance was validated using a confusion matrix, achieving an impressive 92.5 % accuracy with a low false negative rate. PPINtonus demonstrated a precision of 92.7 %, making it a reliable tool for early PD detection. The non-intrusive and efficient methodology of PPINtonus can significantly benefit developing countries by enabling early diagnosis and improving the quality of life for millions of PD patients through timely intervention and management. | [
"['Varun Reddy']"
] |
null | null | 2406.02609 | null | null | http://arxiv.org/pdf/2406.02609v2 | 2024-07-12T08:15:22Z | 2024-06-03T04:09:36Z | Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation | Continual Test-Time Adaptation (CTTA) aims to adapt a pre-trained model to a sequence of target domains during the test phase without accessing the source data. To adapt to unlabeled data from unknown domains, existing methods rely on constructing pseudo-labels for all samples and updating the model through self-training. However, these pseudo-labels often involve noise, leading to insufficient adaptation. To improve the quality of pseudo-labels, we propose a pseudo-label selection method for CTTA, called Pseudo Labeling Filter (PLF). The key idea of PLF is to keep selecting appropriate thresholds for pseudo-labels and identify reliable ones for self-training. Specifically, we present three principles for setting thresholds during continuous domain learning, including initialization, growth and diversity. Based on these principles, we design Self-Adaptive Thresholding to filter pseudo-labels. Additionally, we introduce a Class Prior Alignment (CPA) method to encourage the model to make diverse predictions for unknown domain samples. Through extensive experiments, PLF outperforms current state-of-the-art methods, proving its effectiveness in CTTA. | [
"['Jiayao Tan' 'Fan Lyu' 'Chenggong Ni' 'Tingliang Feng' 'Fuyuan Hu'\n 'Zhang Zhang' 'Shaochuang Zhao' 'Liang Wang']"
] |
null | null | 2406.02610 | null | null | http://arxiv.org/pdf/2406.02610v1 | 2024-06-03T07:17:18Z | 2024-06-03T07:17:18Z | MoFormer: Multi-objective Antimicrobial Peptide Generation Based on
Conditional Transformer Joint Multi-modal Fusion Descriptor | Deep learning holds a big promise for optimizing existing peptides with more desirable properties, a critical step towards accelerating new drug discovery. Despite the recent emergence of several optimized Antimicrobial peptides(AMP) generation methods, multi-objective optimizations remain still quite challenging for the idealism-realism tradeoff. Here, we establish a multi-objective AMP synthesis pipeline (MoFormer) for the simultaneous optimization of multi-attributes of AMPs. MoFormer improves the desired attributes of AMP sequences in a highly structured latent space, guided by conditional constraints and fine-grained multi-descriptor.We show that MoFormer outperforms existing methods in the generation task of enhanced antimicrobial activity and minimal hemolysis. We also utilize a Pareto-based non-dominated sorting algorithm and proxies based on large model fine-tuning to hierarchically rank the candidates. We demonstrate substantial property improvement using MoFormer from two perspectives: (1) employing molecular simulations and scoring interactions among amino acids to decipher the structure and functionality of AMPs; (2) visualizing latent space to examine the qualities and distribution features, verifying an effective means to facilitate multi-objective optimization AMPs with design constraints | [
"['Li Wang' 'Xiangzheng Fu' 'Jiahao Yang' 'Xinyi Zhang' 'Xiucai Ye'\n 'Yiping Liu' 'Tetsuya Sakurai' 'Xiangxiang Zeng']"
] |
null | null | 2406.02611 | null | null | http://arxiv.org/pdf/2406.02611v1 | 2024-06-03T07:56:58Z | 2024-06-03T07:56:58Z | LOLA: LLM-Assisted Online Learning Algorithm for Content Experiments | In the rapidly evolving digital content landscape, media firms and news publishers require automated and efficient methods to enhance user engagement. This paper introduces the LLM-Assisted Online Learning Algorithm (LOLA), a novel framework that integrates Large Language Models (LLMs) with adaptive experimentation to optimize content delivery. Leveraging a large-scale dataset from Upworthy, which includes 17,681 headline A/B tests aimed at evaluating the performance of various headlines associated with the same article content, we first investigate three broad pure-LLM approaches: prompt-based methods, embedding-based classification models, and fine-tuned open-source LLMs. Our findings indicate that prompt-based approaches perform poorly, achieving no more than 65% accuracy in identifying the catchier headline among two options. In contrast, OpenAI-embedding-based classification models and fine-tuned Llama-3-8b models achieve comparable accuracy, around 82-84%, though still falling short of the performance of experimentation with sufficient traffic. We then introduce LOLA, which combines the best pure-LLM approach with the Upper Confidence Bound algorithm to adaptively allocate traffic and maximize clicks. Our numerical experiments on Upworthy data show that LOLA outperforms the standard A/B testing method (the current status quo at Upworthy), pure bandit algorithms, and pure-LLM approaches, particularly in scenarios with limited experimental traffic or numerous arms. Our approach is both scalable and broadly applicable to content experiments across a variety of digital settings where firms seek to optimize user engagement, including digital advertising and social media recommendations. | [
"['Zikun Ye' 'Hema Yoganarasimhan' 'Yufeng Zheng']"
] |
null | null | 2406.02612 | null | null | http://arxiv.org/pdf/2406.02612v1 | 2024-06-03T08:13:47Z | 2024-06-03T08:13:47Z | Is Data Valuation Learnable and Interpretable? | Measuring the value of individual samples is critical for many data-driven tasks, e.g., the training of a deep learning model. Recent literature witnesses the substantial efforts in developing data valuation methods. The primary data valuation methodology is based on the Shapley value from game theory, and various methods are proposed along this path. {Even though Shapley value-based valuation has solid theoretical basis, it is entirely an experiment-based approach and no valuation model has been constructed so far.} In addition, current data valuation methods ignore the interpretability of the output values, despite an interptable data valuation method is of great helpful for applications such as data pricing. This study aims to answer an important question: is data valuation learnable and interpretable? A learned valuation model have several desirable merits such as fixed number of parameters and knowledge reusability. An intrepretable data valuation model can explain why a sample is valuable or invaluable. To this end, two new data value modeling frameworks are proposed, in which a multi-layer perception~(MLP) and a new regression tree are utilized as specific base models for model training and interpretability, respectively. Extensive experiments are conducted on benchmark datasets. {The experimental results provide a positive answer for the question.} Our study opens up a new technical path for the assessing of data values. Large data valuation models can be built across many different data-driven tasks, which can promote the widespread application of data valuation. | [
"['Ou Wu' 'Weiyao Zhu' 'Mengyang Li']"
] |
null | null | 2406.02613 | null | null | http://arxiv.org/pdf/2406.02613v1 | 2024-06-03T08:23:45Z | 2024-06-03T08:23:45Z | ACCO: Accumulate while you Communicate, Hiding Communications in
Distributed LLM Training | Training Large Language Models (LLMs) relies heavily on distributed implementations, employing multiple GPUs to compute stochastic gradients on model replicas in parallel. However, synchronizing gradients in data parallel settings induces a communication overhead increasing with the number of distributed workers, which can impede the efficiency gains of parallelization. To address this challenge, optimization algorithms reducing inter-worker communication have emerged, such as local optimization methods used in Federated Learning. While effective in minimizing communication overhead, these methods incur significant memory costs, hindering scalability: in addition to extra momentum variables, if communications are only allowed between multiple local optimization steps, then the optimizer's states cannot be sharded among workers. In response, we propose $textbf{AC}$cumulate while $textbf{CO}$mmunicate ($texttt{ACCO}$), a memory-efficient optimization algorithm tailored for distributed training of LLMs. $texttt{ACCO}$ allows to shard optimizer states across workers, overlaps gradient computations and communications to conceal communication costs, and accommodates heterogeneous hardware. Our method relies on a novel technique to mitigate the one-step delay inherent in parallel execution of gradient computations and communications, eliminating the need for warmup steps and aligning with the training dynamics of standard distributed optimization while converging faster in terms of wall-clock time. We demonstrate the effectiveness of $texttt{ACCO}$ on several LLMs training and fine-tuning tasks. | [
"['Adel Nabli' 'Louis Fournier' 'Pierre Erbacher' 'Louis Serrano'\n 'Eugene Belilovsky' 'Edouard Oyallon']"
] |
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