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Title: Fact Checking with Insufficient Evidence Abstract: Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enou... |
Title: LatentGAN Autoencoder: Learning Disentangled Latent Distribution Abstract: In autoencoder, the encoder generally approximates the latent distribution over the dataset, and the decoder generates samples using this learned latent distribution. There is very little control over the latent vector as using the random... |
Title: RL4ReAl: Reinforcement Learning for Register Allocation Abstract: We propose a novel solution for the Register Allocation problem, leveraging multi-agent hierarchical Reinforcement Learning. We formalize the constraints that precisely define the problem for a given instruction-set architecture, while ensuring th... |
Title: A Survey on Dropout Methods and Experimental Verification in Recommendation Abstract: Overfitting is a common problem in machine learning, which means the model too closely fits the training data while performing poorly in the test data. Among various methods of coping with overfitting, dropout is one of the rep... |
Title: A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts Abstract: Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affectin... |
Title: Automating Reinforcement Learning with Example-based Resets Abstract: Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge. However, existing reinforcement learning algorithms assume an episodic setting, in which the agent resets ... |
Title: Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation Abstract: The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works f... |
Title: Split Hierarchical Variational Compression Abstract: Variational autoencoders (VAEs) have witnessed great success in performing the compression of image datasets. This success, made possible by the bits-back coding framework, has produced competitive compression performance across many benchmarks. However, despi... |
Title: Complex-Valued Autoencoders for Object Discovery Abstract: Object-centric representations form the basis of human perception, and enable us to reason about the world and to systematically generalize to new settings. Currently, most works on unsupervised object discovery focus on slot-based approaches, which expl... |
Title: P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior Abstract: Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time. Based on knowledge about the high regularity of real 3... |
Title: A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data Abstract: This paper investigates the possibility of high resolution mapping of PM2.5 concentration over Tehran city using high resolution satellite AOD (MAIAC) retrievals. For this purpose, a framework ... |
Title: Self-supervised learning -- A way to minimize time and effort for precision agriculture? Abstract: Machine learning, satellites or local sensors are key factors for a sustainable and resource-saving optimisation of agriculture and proved its values for the management of agricultural land. Up to now, the main foc... |
Title: GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes Abstract: The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncerta... |
Title: MetaAudio: A Few-Shot Audio Classification Benchmark Abstract: Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification. This work aims to alleviate this reliance on image-based benchmarks ... |
Title: SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model Abstract: A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affects drug... |
Title: Positive and Negative Critiquing for VAE-based Recommenders Abstract: Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has propose... |
Title: Hybrid Predictive Coding: Inferring, Fast and Slow Abstract: Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising "prediction errors" - the differences between predicted and observed data. Implicit in this proposal is ... |
Title: Optimising Communication Overhead in Federated Learning Using NSGA-II Abstract: Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substa... |
Title: Penalised FTRL With Time-Varying Constraints Abstract: In this paper we extend the classical Follow-The-Regularized-Leader (FTRL) algorithm to encompass time-varying constraints, through adaptive penalization. We establish sufficient conditions for the proposed Penalized FTRL algorithm to achieve $O(\sqrt{t})$ r... |
Title: Abstractive summarization of hospitalisation histories with transformer networks Abstract: In this paper we present a novel approach to abstractive summarization of patient hospitalisation histories. We applied an encoder-decoder framework with Longformer neural network as an encoder and BERT as a decoder. Our e... |
Title: Model Based Meta Learning of Critics for Policy Gradients Abstract: Being able to seamlessly generalize across different tasks is fundamental for robots to act in our world. However, learning representations that generalize quickly to new scenarios is still an open research problem in reinforcement learning. In ... |
Title: SNUG: Self-Supervised Neural Dynamic Garments Abstract: We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment deformations are trained using supervised strategies that require large datasets, u... |
Title: Neural Computing with Coherent Laser Networks Abstract: We show that a coherent network of lasers exhibits emergent neural computing capabilities. The proposed scheme is built on harnessing the collective behavior of laser networks for storing a number of phase patterns as stable fixed points of the governing dy... |
Title: A Set Membership Approach to Discovering Feature Relevance and Explaining Neural Classifier Decisions Abstract: Neural classifiers are non linear systems providing decisions on the classes of patterns, for a given problem they have learned. The output computed by a classifier for each pattern constitutes an appr... |
Title: Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators Abstract: We present a specialized scenario generation method that utilizes forecast information to generate scenarios for the particular usage in day-ahead scheduling problems.... |
Title: Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization Abstract: We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different ... |
Title: Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors Abstract: Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation. New kinds of abusive language contin... |
Title: Multilingual and Multimodal Abuse Detection Abstract: The presence of abusive content on social media platforms is undesirable as it severely impedes healthy and safe social media interactions. While automatic abuse detection has been widely explored in textual domain, audio abuse detection still remains unexplo... |
Title: Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions Abstract: We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private and system goals by balancing ... |
Title: Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games Abstract: We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns... |
Title: Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systems Abstract: We study the Bayesian inverse problem of inferring the Biot number, a spatio-temporal heat-flux parameter in a PDE model. This is an ill-posed problem where standard optimisa... |
Title: Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets Abstract: Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the dete... |
Title: Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data Abstract: Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on automatic cancer subtyping systems have been p... |
Title: Design Guidelines for Inclusive Speaker Verification Evaluation Datasets Abstract: Speaker verification (SV) provides billions of voice-enabled devices with access control, and ensures the security of voice-driven technologies. As a type of biometrics, it is necessary that SV is unbiased, with consistent and rel... |
Title: Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation Abstract: Recent research has shown that generative models with highly disentangled representations fail to generalise to unseen combination of generative factor values. These findings contradict earlier research which sh... |
Title: SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering Abstract: While Visual Question Answering (VQA) has progressed rapidly, previous works raise concerns about robustness of current VQA models. In this work, we study the robustness of VQA models from a novel pers... |
Title: Aggregating distribution forecasts from deep ensembles Abstract: The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting. In particular, a variety of deep learning approaches has been proposed, with forecast distributions obtained as output of... |
Title: Is it worth the effort? Understanding and contextualizing physical metrics in soccer Abstract: We present a framework that gives a deep insight into the link between physical and technical-tactical aspects of soccer and it allows associating physical performance with value generation thanks to a top-down approac... |
Title: Learning new physics efficiently with nonparametric methods Abstract: We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any conti... |
Title: Learning Generalizable Dexterous Manipulation from Human Grasp Affordance Abstract: Dexterous manipulation with a multi-finger hand is one of the most challenging problems in robotics. While recent progress in imitation learning has largely improved the sample efficiency compared to Reinforcement Learning, the l... |
Title: SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications Abstract: Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency of high-dimensional models and addre... |
Title: Nearly minimax robust estimator of the mean vector by iterative spectral dimension reduction Abstract: We study the problem of robust estimation of the mean vector of a sub-Gaussian distribution. We introduce an estimator based on spectral dimension reduction (SDR) and establish a finite sample upper bound on it... |
Title: A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery Abstract: Despite the breakthrough deep learning performances achieved for automatic object detection, small target detection is still a challenging problem, especially when looking at fast and accurate solutions suitable f... |
Title: Can language models learn from explanations in context? Abstract: Large language models can perform new tasks by adapting to a few in-context examples. For humans, rapid learning from examples can benefit from explanations that connect examples to task principles. We therefore investigate whether explanations of... |
Title: Multi-Scale Representation Learning on Proteins Abstract: Proteins are fundamental biological entities mediating key roles in cellular function and disease. This paper introduces a multi-scale graph construction of a protein -- HoloProt -- connecting surface to structure and sequence. The surface captures coarse... |
Title: MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering Abstract: Collaborative filtering (CF) is widely used by personalized recommendation systems, which aims to predict the preference of users with historical user-item interactions. In recent years, Graph Neural Networks (GNNs) ... |
Title: IFTT-PIN: Demonstrating the Self-Calibration Paradigm on a PIN-Entry Task Abstract: We demonstrate IFTT-PIN, a self-calibrating version of the PIN-entry method introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets and assigned a color respectively. To communicate their digit, users press t... |
Title: Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling Abstract: Evaluating the performance of autonomous vehicles (AV) and their complex subsystems to high precision under naturalistic circumstances remains a challenge, especially when failure or... |
Title: Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs Abstract: Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with di... |
Title: Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis Abstract: Generating sub-optimal synthesis transformation sequences ("synthesis recipe") is an important problem in logic synthesis. Manually crafted synthesis recipes have poor quality. State-of-the art machine learning (ML) works to generate synt... |
Title: Jump-Start Reinforcement Learning Abstract: Reinforcement learning (RL) provides a theoretical framework for continuously improving an agent's behavior via trial and error. However, efficiently learning policies from scratch can be very difficult, particularly for tasks with exploration challenges. In such setti... |
Title: Data-driven Influence Based Clustering of Dynamical Systems Abstract: Community detection is a challenging and relevant problem in various disciplines of science and engineering like power systems, gene-regulatory networks, social networks, financial networks, astronomy etc. Furthermore, in many of these applica... |
Title: Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning Abstract: As automatic speech recognition (ASR) systems are now being widely deployed in the wild, the increasing threat of adversarial attacks raises serious questions about the security and reliability of using... |
Title: Learning Speech Emotion Representations in the Quaternion Domain Abstract: The modeling of human emotion expression in speech signals is an important, yet challenging task. The high resource demand of speech emotion recognition models, combined with the the general scarcity of emotion-labelled data are obstacles... |
Title: ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer Abstract: Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent dataset t... |
Title: Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower Abstract: We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a means of efficiently moving scattered objects into a target receptacle. Due to the chaotic nature of aerodynamic forces, a blowing controller must (i) continuall... |
Title: Action-Conditioned Contrastive Policy Pretraining Abstract: Deep visuomotor policy learning achieves promising results in control tasks such as robotic manipulation and autonomous driving, where the action is generated from the visual input by the neural policy. However, it requires a huge number of online inter... |
Title: SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space Abstract: We propose the first SE(3)-equivariant coordinate-based network for learning occupancy fields from point clouds. In contrast to previous shape reconstruction methods that align the input to a regular grid, we operate direct... |
Title: Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification Abstract: The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-moda... |
Title: Comment on "Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care" Abstract: In this paper we examine the claims made by Bullock et. al. on the applicability of black-box injury risk approaches in the sports injury domain. ... |
Title: Zero-shot Blind Image Denoising via Implicit Neural Representations Abstract: Recent denoising algorithms based on the "blind-spot" strategy show impressive blind image denoising performances, without utilizing any external dataset. While the methods excel in recovering highly contaminated images, we observe tha... |
Title: OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses Abstract: Dataset bias and spurious correlations can significantly impair generalization in deep neural networks. Many prior efforts have addressed this problem using either alternative loss functions or sampling strategies that focus on rare patt... |
Title: Imaging Conductivity from Current Density Magnitude using Neural Networks Abstract: Conductivity imaging represents one of the most important tasks in medical imaging. In this work we develop a neural network based reconstruction technique for imaging the conductivity from the magnitude of the internal current d... |
Title: Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis Abstract: We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view hierarchies required by previous ... |
Title: Improving Voice Trigger Detection with Metric Learning Abstract: Voice trigger detection is an important task, which enables activating a voice assistant when a target user speaks a keyword phrase. A detector is typically trained on speech data independent of speaker information and used for the voice trigger de... |
Title: Configuration Path Control Abstract: Reinforcement learning methods often produce brittle policies -- policies that perform well during training, but generalize poorly beyond their direct training experience, thus becoming unstable under small disturbances. To address this issue, we propose a method for stabiliz... |
Title: Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge Abstract: Deploying generative machine learning techniques to generate novel chemical structures based on molecular fingerprint representation has been well established in molecular design. Typically, seque... |
Title: Adversarial Robustness through the Lens of Convolutional Filters Abstract: Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models to make wrong predictions with high confidence. An common... |
Title: Training-Free Robust Multimodal Learning via Sample-Wise Jacobian Regularization Abstract: Multimodal fusion emerges as an appealing technique to improve model performances on many tasks. Nevertheless, the robustness of such fusion methods is rarely involved in the present literature. In this paper, we propose a... |
Title: Discovering and forecasting extreme events via active learning in neural operators Abstract: Extreme events in society and nature, such as pandemic spikes or rogue waves, can have catastrophic consequences. Characterizing extremes is difficult as they occur rarely, arise from seemingly benign conditions, and bel... |
Title: Pareto-optimal clustering with the primal deterministic information bottleneck Abstract: At the heart of both lossy compression and clustering is a trade-off between the fidelity and size of the learned representation. Our goal is to map out and study the Pareto frontier that quantifies this trade-off. We focus ... |
Title: Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption Abstract: Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially ... |
Title: Deep Graphic FBSDEs for Opinion Dynamics Stochastic Control Abstract: In this paper, we present a scalable deep learning approach to solve opinion dynamics stochastic optimal control problems with mean field term coupling in the dynamics and cost function. Our approach relies on the probabilistic representation ... |
Title: In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation Abstract: Proteins in complex with small molecule ligands represent the core of structure-based drug discovery. However, three-dimensional representations are absent from most deep-learning-based generative models. We h... |
Title: Service resource allocation problem in the IoT driven personalized healthcare information platform Abstract: With real-time monitoring of the personalized healthcare condition, the IoT wearables collect the health data and transfer it to the healthcare information platform. The platform processes the data into h... |
Title: Emphasis on the Minimization of False Negatives or False Positives in Binary Classification Abstract: The minimization of specific cases in binary classification, such as false negatives or false positives, grows increasingly important as humans begin to implement more machine learning into current products. Whi... |
Title: Prosodic Alignment for off-screen automatic dubbing Abstract: The goal of automatic dubbing is to perform speech-to-speech translation while achieving audiovisual coherence. This entails isochrony, i.e., translating the original speech by also matching its prosodic structure into phrases and pauses, especially w... |
Title: Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures Abstract: We show direct and conceptually simple reductions between the classical learning with errors (LWE) problem and its continuous analog, CLWE (Bruna, Regev, Song and Tang, STOC 2021). This allows us to bring to bear the powerful... |
Title: DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning Abstract: Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games. Among these games, DouDizhu, a popular card game in China, is very challenging due t... |
Title: FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons Abstract: With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfo... |
Title: Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence Abstract: We design fast algorithms for repeatedly sampling from strongly Rayleigh distributions, which include random spanning tree distributions and determinantal point processes. For a graph $... |
Title: Greedier is Better: Selecting Multiple Neighbors per Iteration for Sparse Subspace Clustering Abstract: Sparse subspace clustering (SSC) using greedy-based neighbor selection, such as orthogonal matching pursuit (OMP), has been known as a popular computationally-efficient alternative to the popular L1-minimizati... |
Title: PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations Abstract: In this work, a Gaussian process regression(GPR) model incorporated with given physical information in partial differential equations(PDEs) is developed: physics-a... |
Title: Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise Abstract: We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several popular... |
Title: Consensual Aggregation on Random Projected High-dimensional Features for Regression Abstract: In this paper, we present a study of a kernel-based consensual aggregation on randomly projected high-dimensional features of predictions for regression. The aggregation scheme is composed of two steps: the high-dimensi... |
Title: Efficient Test-Time Model Adaptation without Forgetting Abstract: Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important for deep models when the test environment changes f... |
Title: Data-Driven Approach for Log Instruction Quality Assessment Abstract: In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messages: the developer provides hints to the (unknown) operator, wh... |
Title: Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction Abstract: Stock market plays an important role in the economic development. Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors. The traditional... |
Title: Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020 Abstract: Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in modeling graph struc... |
Title: Federated Reinforcement Learning with Environment Heterogeneity Abstract: We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction. We stress the constraint of environ... |
Title: Failure Identification from Unstable Log Data using Deep Learning Abstract: The reliability of cloud platforms is of significant relevance because society increasingly relies on complex software systems running on the cloud. To improve it, cloud providers are automating various maintenance tasks, with failure id... |
Title: Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction Abstract: Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, it faces two main challenges. First, different metro stations, e.g. transfer sta... |
Title: CHIEF: Clustering with Higher-order Motifs in Big Networks Abstract: Clustering a group of vertices in networks facilitates applications across different domains, such as social computing and Internet of Things. However, challenges arises for clustering networks with increased scale. This paper proposes a soluti... |
Title: CAIPI in Practice: Towards Explainable Interactive Medical Image Classification Abstract: Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-o... |
Title: Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs Abstract: The emerging Graph Convolutional Network (GCN) has now been widely used in many domains, and it is challenging to improve the efficiencies of applications by accelerating the GCN trainings. For the sparsity nature a... |
Title: Double Descent in Random Feature Models: Precise Asymptotic Analysis for General Convex Regularization Abstract: We prove rigorous results on the double descent phenomenon in random features (RF) model by employing the powerful Convex Gaussian Min-Max Theorem (CGMT) in a novel multi-level manner. Using this tech... |
Title: Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations Abstract: Contrastive learning is a highly effective method for learning representations from unlabeled data. Recent works show that contrastive representations can transfer across domains, leading ... |
Title: Learning to Adapt Clinical Sequences with Residual Mixture of Experts Abstract: Clinical event sequences in Electronic Health Records (EHRs) record detailed information about the patient condition and patient care as they occur in time. Recent years have witnessed increased interest of machine learning community... |
Title: Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices Abstract: This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power spectral density... |
Title: VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time Series Abstract: One of the latest self-supervised learning (SSL) methods, VICReg, showed a great performance both in the linear evaluation and the fine-tuning evaluation. However, VICReg is propose... |
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