Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores

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Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

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Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

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Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

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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 ...

We propose a test -time adaptation for 6D object pose tracking that learns to adapt a pre -trained model to track the 6D pose of novel objects. We consider the problem of 6D object pose tracking as a 3D keypoint detection and matching task and present a mo ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores

Chattez avec Graph Search

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Computational design of complex protein folds and soluble analogs of membrane proteins

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Learning to Reason with Neural Networks: Principles and Methods

Fair Decentralized Learning

Machine learning-aided generative molecular design

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

Machine Learning Security Against Data Poisoning: Are We There Yet?

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

Robust machine learning for neuroscientific inference

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

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

Test-time adaptation for 6D pose tracking

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning