Learning Trajectory Dependencies for Human Motion Prediction

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Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

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Thermal scene reconstruction holds great potential for various applications, such as building energy analysis and non-destructive infrastructure testing. However, existing methods rely on dense scene measurements and use RGB images for 3D reconstruction, i ...

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Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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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 ...

Learning Trajectory Dependencies for Human Motion Prediction

Chattez avec Graph Search

Topics in statistical physics of high-dimensional machine learning

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Self-supervised and few-shot learning for robust bioaerosol monitoring

Network time series forecasting in photovoltaics power production

Domain shift, domain adaptation, and generalization: A focus on MRI

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Context selectivity with dynamic availability enables lifelong continual learning

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Robust machine learning for neuroscientific inference

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

Sparse autoregressive neural networks for classical spin systems

Probing the latent hierarchical structure of data via diffusion models

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Driving and suppressing the human language network using large language models