text stringlengths 0 4.09k |
|---|
Title: Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems Abstract: Model-free deep-reinforcement-based learning algorithms have been applied to a range of COPs~\cite{bello2016neural}~\cite{kool2018attention}~\cite{nazari2018reinforcement}. However, these approaches suffer from two key challen... |
Title: Label-Enhanced Graph Neural Network for Semi-supervised Node Classification Abstract: Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNN... |
Title: Are classical neural networks quantum? Abstract: Neural networks are being used to improve the probing of the state spaces of many particle systems as approximations to wavefunctions and in order to avoid the recurring sign problem of quantum monte-carlo. One may ask whether the usual classical neural networks h... |
Title: Differentiable Invariant Causal Discovery Abstract: Learning causal structure from observational data is a fundamental challenge in machine learning. The majority of commonly used differentiable causal discovery methods are non-identifiable, turning this problem into a continuous optimization task prone to data ... |
Title: Improvements to Supervised EM Learning of Shared Kernel Models by Feature Space Partitioning Abstract: Expectation maximisation (EM) is usually thought of as an unsupervised learning method for estimating the parameters of a mixture distribution, however it can also be used for supervised learning when class lab... |
Title: Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees Abstract: Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of frac... |
Title: k-Means Maximum Entropy Exploration Abstract: Exploration in high-dimensional, continuous spaces with sparse rewards is an open problem in reinforcement learning. Artificial curiosity algorithms address this by creating rewards that lead to exploration. Given a reinforcement learning algorithm capable of maximiz... |
Title: Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks Abstract: Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized NNs can easily o... |
Title: Optimizing Intermediate Representations of Generative Models for Phase Retrieval Abstract: Phase retrieval is the problem of reconstructing images from magnitude-only measurements. In many real-world applications the problem is underdetermined. When training data is available, generative models are a new idea to... |
Title: Communication-Efficient Distributionally Robust Decentralized Learning Abstract: Decentralized learning algorithms empower interconnected edge devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator (e.g. an orchestrating basest... |
Title: GlanceNets: Interpretabile, Leak-proof Concept-based Models Abstract: There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A key requirement is that the concepts be interpretable. Existing ... |
Title: Prompt Injection: Parameterization of Fixed Inputs Abstract: Recent works have shown that attaching prompts to the input is effective at conditioning Language Models (LM) to perform specific tasks. However, prompts are always included in the input text during inference, thus incurring substantial computational a... |
Title: Individual health-disease phase diagrams for disease prevention based on machine learning Abstract: Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in mult... |
Title: Semantic Autoencoder and Its Potential Usage for Adversarial Attack Abstract: Autoencoder can give rise to an appropriate latent representation of the input data, however, the representation which is solely based on the intrinsic property of the input data, is usually inferior to express some semantic informatio... |
Title: Improving Ads-Profitability Using Traffic-Fingerprints Abstract: This paper introduces the concept of traffic-fingerprints, i.e., normalized 24-dimensional vectors representing a distribution of daily traffic on a web page. Using k-means clustering we show that similarity of traffic-fingerprints is related to th... |
Title: Automatic differentiation of nonsmooth iterative algorithms Abstract: Differentiation along algorithms, i.e., piggyback propagation of derivatives, is now routinely used to differentiate iterative solvers in differentiable programming. Asymptotics is well understood for many smooth problems but the nondifferenti... |
Title: Comparing interpretation methods in mental state decoding analyses with deep learning models Abstract: Deep learning (DL) methods find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (such as accepting or rejecting a gamble) and brain activi... |
Title: A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting Abstract: We present a new method that includes three key components of distributed optimization and federated learning: variance reduction of stochastic gradients, compressed communication, a... |
Title: GSR: A Generalized Symbolic Regression Approach Abstract: Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attempts to... |
Title: HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness Abstract: The process of optimizing the latency of DNN operators with ML models and hardware-in-the-loop, called auto-tuning, has established itself as a pervasive method for the deployment of neural networks. From a search spa... |
Title: Few-Shot Unlearning by Model Inversion Abstract: We consider the problem of machine unlearning to erase a target dataset, which causes an unwanted behavior, from the trained model when the training dataset is not given. Previous works have assumed that the target dataset indicates all the training data imposing ... |
Title: Secure Federated Clustering Abstract: We consider a foundational unsupervised learning task of $k$-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients. We develop SecFC, which is a secure federated clustering algorithm that simultaneously achiev... |
Title: Graph-level Neural Networks: Current Progress and Future Directions Abstract: Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of graphs instead of a single graph. Traditional graph-le... |
Title: VC Theoretical Explanation of Double Descent Abstract: There has been growing interest in generalization performance of large multilayer neural networks that can be trained to achieve zero training error, while generalizing well on test data. This regime is known as 'second descent' and it appears to contradict ... |
Title: Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series Abstract: This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based method and in sharp contrast to the existing wi... |
Title: MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation Abstract: Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning m... |
Title: DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation Abstract: Recent advancements in the field of magnetic resonance imaging (MRI) have enabled large-scale collaboration among clinicians and researchers for neuroimaging tasks. However, researchers are often forced to use outdated and s... |
Title: Gluing Neural Networks Symbolically Through Hyperdimensional Computing Abstract: Hyperdimensional Computing affords simple, yet powerful operations to create long Hyperdimensional Vectors (hypervectors) that can efficiently encode information, be used for learning, and are dynamic enough to be modified on the fl... |
Title: itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection Abstract: Recently, point-cloud based 3D object detectors have achieved remarkable progress. However, most studies are limited to the development of deep learning architectures for improving only their accuracy. In this paper, we pro... |
Title: Variational Transfer Learning using Cross-Domain Latent Modulation Abstract: To successfully apply trained neural network models to new domains, powerful transfer learning solutions are essential. We propose to introduce a novel cross-domain latent modulation mechanism to a variational autoencoder framework so a... |
Title: Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game Abstract: Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms h... |
Title: Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning Abstract: This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron cor... |
Title: Rethinking Graph Neural Networks for Anomaly Detection Abstract: Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum.... |
Title: Certifying Some Distributional Fairness with Subpopulation Decomposition Abstract: Extensive efforts have been made to understand and improve the fairness of machine learning models based on observational metrics, especially in high-stakes domains such as medical insurance, education, and hiring decisions. Howev... |
Title: Sepsis Prediction with Temporal Convolutional Networks Abstract: We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective analysis of patients admitted to intensive care unit who did not fall u... |
Title: Post-hoc Concept Bottleneck Models Abstract: Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model "sees"... |
Title: Data Banzhaf: A Data Valuation Framework with Maximal Robustness to Learning Stochasticity Abstract: This paper studies the robustness of data valuation to noisy model performance scores. Particularly, we find that the inherent randomness of the widely used stochastic gradient descent can cause existing data val... |
Title: Few-Shot Diffusion Models Abstract: Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable models with remarkable sample generation quality and training stability. These properties can be attributed to parameter sharing in the generative hierarchy, as well as a parameter-free d... |
Title: Critic Sequential Monte Carlo Abstract: We introduce CriticSMC, a new algorithm for planning as inference built from a novel composition of sequential Monte Carlo with learned soft-Q function heuristic factors. This algorithm is structured so as to allow using large numbers of putative particles leading to effic... |
Title: A Comparative Study on Energy Consumption Models for Drones Abstract: Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature. However, a general consensus on the energy consumption model is yet to be reached at present. As a result,... |
Title: Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning Abstract: Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the... |
Title: GLDQN: Explicitly Parameterized Quantile Reinforcement Learning for Waste Reduction Abstract: We study the problem of restocking a grocery store's inventory with perishable items over time, from a distributional point of view. The objective is to maximize sales while minimizing waste, with uncertainty about the ... |
Title: Posterior and Computational Uncertainty in Gaussian Processes Abstract: Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. This additional source of uncertainty, due to limited computati... |
Title: Holistic Generalized Linear Models Abstract: Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The $\tex... |
Title: Continual Object Detection: A review of definitions, strategies, and challenges Abstract: The field of Continual Learning investigates the ability to learn consecutive tasks without losing performance on those previously learned. Its focus has been mainly on incremental classification tasks. We believe that rese... |
Title: StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis Abstract: Text-to-Speech (TTS) has recently seen great progress in synthesizing high-quality speech owing to the rapid development of parallel TTS systems, but producing speech with naturalistic prosodic variations, speakin... |
Title: FBM: Fast-Bit Allocation for Mixed-Precision Quantization Abstract: Quantized neural networks are well known for reducing latency, power consumption, and model size without significant degradation in accuracy, making them highly applicable for systems with limited resources and low power requirements. Mixed prec... |
Title: Fairness in the First Stage of Two-Stage Recommender Systems Abstract: Many large-scale recommender systems consist of two stages, where the first stage focuses on efficiently generating a small subset of promising candidates from a huge pool of items for the second-stage model to curate final recommendations fr... |
Title: Learning Risk-Averse Equilibria in Multi-Agent Systems Abstract: In multi-agent systems, intelligent agents are tasked with making decisions that have optimal outcomes when the actions of the other agents are as expected, whilst also being prepared for unexpected behaviour. In this work, we introduce a new risk-... |
Title: Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity Abstract: Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device. While prior FL optimizations tackled the d... |
Title: Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation Abstract: Generalizability is seen as one of the major challenges in deep learning, in particular in the domain of medical imaging, where a change of hospital or in imaging routines can lead to a complete failure... |
Title: Connecting adversarial attacks and optimal transport for domain adaptation Abstract: We present a novel algorithm for domain adaptation using optimal transport. In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the target domain. In our method, we use optimal transpo... |
Title: Analysis of Augmentations for Contrastive ECG Representation Learning Abstract: This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning of electrocardiogram (ECG) signals and identifies the best parameters. The baseline of our proposed self-super... |
Title: Fooling SHAP with Stealthily Biased Sampling Abstract: SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arb... |
Title: PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps Abstract: Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can... |
Title: Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos Abstract: The interest for video anomaly detection systems has gained traction for the past few years. The current approaches use deep learning to perform anomaly detection in videos, but this approach has multiple problems. For starters, dee... |
Title: Neural Optimal Transport with General Cost Functionals Abstract: We present a novel neural-networks-based algorithm to compute optimal transport (OT) plans and maps for general cost functionals. The algorithm is based on a saddle point reformulation of the OT problem and generalizes prior OT methods for weak and... |
Title: Designing Rewards for Fast Learning Abstract: To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward function for the environment, arguably the most important knob designers have in interacting with RL agents. Although many reward functions induce the same optimal beha... |
Title: Minimax Optimal Online Imitation Learning via Replay Estimation Abstract: Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infinite sample regime, exact moment matching achieves value eq... |
Title: A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin Abstract: Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculati... |
Title: A hybrid approach to seismic deblending: when physics meets self-supervision Abstract: To limit the time, cost, and environmental impact associated with the acquisition of seismic data, in recent decades considerable effort has been put into so-called simultaneous shooting acquisitions, where seismic sources are... |
Title: Attention Flows for General Transformers Abstract: In this paper, we study the computation of how much an input token in a Transformer model influences its prediction. We formalize a method to construct a flow network out of the attention values of encoder-only Transformer models and extend it to general Transfo... |
Title: Truly Deterministic Policy Optimization Abstract: In this paper, we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape. By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic ... |
Title: Reinforcement Learning with a Terminator Abstract: We present the problem of reinforcement learning with exogenous termination. We define the Termination Markov Decision Process (TerMDP), an extension of the MDP framework, in which episodes may be interrupted by an external non-Markovian observer. This formulati... |
Title: Optimistic Whittle Index Policy: Online Learning for Restless Bandits Abstract: Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled. However, solving RMABs req... |
Title: Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations Abstract: The paper has two major themes. The first part of the paper establishes certain general results for infinite-dimensional optimization problems on Hilbert spaces. These results cover the classical ... |
Title: Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning Abstract: We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independen... |
Title: Searching for the Essence of Adversarial Perturbations Abstract: Neural networks have achieved the state-of-the-art performance on various machine learning fields, yet the incorporation of malicious perturbations with input data (adversarial example) is able to fool neural networks' predictions. This would lead ... |
Title: Adapting Rapid Motor Adaptation for Bipedal Robots Abstract: Recent advances in legged locomotion have enabled quadrupeds to walk on challenging terrains. However, bipedal robots are inherently more unstable and hence it's harder to design walking controllers for them. In this work, we leverage recent advances i... |
Title: Efficient $Φ$-Regret Minimization in Extensive-Form Games via Online Mirror Descent Abstract: A conceptually appealing approach for learning Extensive-Form Games (EFGs) is to convert them to Normal-Form Games (NFGs). This approach enables us to directly translate state-of-the-art techniques and analyses in NFGs ... |
Title: Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer Abstract: Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common histologic subtypes of non-small-cell lung cance... |
Title: Self-Supervised Visual Representation Learning with Semantic Grouping Abstract: In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still,... |
Title: Testing for Geometric Invariance and Equivariance Abstract: Invariant and equivariant models incorporate the symmetry of an object to be estimated (here non-parametric regression functions $f : \mathcal{X} \rightarrow \mathbb{R}$). These models perform better (with respect to $L^2$ loss) and are increasingly bei... |
Title: MetaSSD: Meta-Learned Self-Supervised Detection Abstract: Deep learning-based symbol detector gains increasing attention due to the simple algorithm design than the traditional model-based algorithms such as Viterbi and BCJR. The supervised learning framework is often employed to predict the input symbols, where... |
Title: Kernel Neural Optimal Transport Abstract: We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak quadratic cost might learn fake plans which are not optimal. To resolve this issue, we introdu... |
Title: Federated X-Armed Bandit Abstract: This work establishes the first framework of federated $\mathcal{X}$-armed bandit, where different clients face heterogeneous local objective functions defined on the same domain and are required to collaboratively figure out the global optimum. We propose the first federated a... |
Title: Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance? Abstract: Technological and computational advances continuously drive forward the broad field of deep learning. In recent years, the derivation of quantities describing theuncertainty in the prediction - which natur... |
Title: bsnsing: A decision tree induction method based on recursive optimal boolean rule composition Abstract: This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process, and develops an efficient search algorithm that is able to solve p... |
Title: Pooling Revisited: Your Receptive Field is Suboptimal Abstract: The size and shape of the receptive field determine how the network aggregates local information and affect the overall performance of a model considerably. Many components in a neural network, such as kernel sizes and strides for convolution and po... |
Title: Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning Abstract: Multi-Agent Reinforcement Learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement learning in a multi-agent setting... |
Title: Re-parameterizing Your Optimizers rather than Architectures Abstract: The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers (e.g., SGD). In this pa... |
Title: Multi-Game Decision Transformers Abstract: A longstanding goal of the field of AI is a strategy for compiling diverse experience into a highly capable, generalist agent. In the subfields of vision and language, this was largely achieved by scaling up transformer-based models and training them on large, diverse d... |
Title: Conformal Credal Self-Supervised Learning Abstract: In semi-supervised learning, the paradigm of self-training refers to the idea of learning from pseudo-labels suggested by the learner itself. Across various domains, corresponding methods have proven effective and achieve state-of-the-art performance. However, ... |
Title: VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models Abstract: Recent advances in vision-language pre-training (VLP) have demonstrated impressive performance in a range of vision-language (VL) tasks. However, there exist several challenges for measuring the community's progress in building general ... |
Title: RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression Abstract: Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks. Unlike classification, in regression the labels are continuous, potentially boun... |
Title: Non-convex online learning via algorithmic equivalence Abstract: We study an algorithmic equivalence technique between nonconvex gradient descent and convex mirror descent. We start by looking at a harder problem of regret minimization in online non-convex optimization. We show that under certain geometric and s... |
Title: Few-Shot Adaptation of Pre-Trained Networks for Domain Shift Abstract: Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data. Recent test-time adaptation methods update batch normalization layers of pre-trained source models depl... |
Title: Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models Abstract: Pre-trained masked language models successfully perform few-shot learning by formulating downstream tasks as text infilling. However, as a strong alternative in full-shot settings, discriminative pre-trained models like ELECTRA... |
Title: Automatic Short Math Answer Grading via In-context Meta-learning Abstract: Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vec... |
Title: A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction Abstract: Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolution... |
Title: Support Recovery in Sparse PCA with Incomplete Data Abstract: We study a practical algorithm for sparse principal component analysis (PCA) of incomplete and noisy data. Our algorithm is based on the semidefinite program (SDP) relaxation of the non-convex $l_1$-regularized PCA problem. We provide theoretical and ... |
Title: Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works Abstract: Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities. The derivative of these solvers is zero or undefined... |
Title: Flowification: Everything is a Normalizing Flow Abstract: We develop a method that can be used to turn any multi-layer perceptron or convolutional network into a normalizing flow. In some cases this requires the addition of uncorrelated noise to the model but in the simplest case no additional parameters. The te... |
Title: Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training Abstract: Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically sparse neural networks from sc... |
Title: DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems Abstract: Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles. Reinforcement learning (RL) on large musculoskeletal models, howev... |
Title: PAC Generalization via Invariant Representations Abstract: One method for obtaining generalizable solutions to machine learning tasks when presented with diverse training environments is to find invariant representations of the data. These are representations of the covariates such that the best model on top of ... |
Title: Do Deep Neural Networks Always Perform Better When Eating More Data? Abstract: Data has now become a shortcoming of deep learning. Researchers in their own fields share the thinking that "deep neural networks might not always perform better when they eat more data," which still lacks experimental validation and ... |
Title: A Review and Evaluation of Elastic Distance Functions for Time Series Clustering Abstract: Time series clustering is the act of grouping time series data without recourse to a label. Algorithms that cluster time series can be classified into two groups: those that employ a time series specific distance measure; ... |
Title: Parameter Efficient Diff Pruning for Bias Mitigation Abstract: In recent years language models have achieved state of the art performance on a wide variety of natural language processing tasks. As these models are continuously growing in size it becomes increasingly important to explore methods to make them more... |
Title: Towards Efficient 3D Object Detection with Knowledge Distillation Abstract: Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from heavy computation overheads. To this end, we explore the potential of knowledge distillation (KD) for developing efficient 3D object detectors, ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.