Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

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Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

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Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double Sérsic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified param ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition (ASR) on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are train ...

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Chattez avec Graph Search

Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

One Code to Predict Them All: Universal Encoding for Inquiry Modeling

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Predicting the long-term collective behaviour of fish pairs with deep learning

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and K-Means Clustering

Driving and suppressing the human language network using large language models

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

On the number of regions of piecewise linear neural networks

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Aggregating Spatial and Photometric Context for Photometric Stereo

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Social Opinion Formation and Decision Making Under Communication Trends

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models