Publications associées à Réseau de neurones artificiels

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Réseau de neurones artificiels

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Aggregating Spatial and Photometric Context for Photometric Stereo

Robust machine learning for neuroscientific inference

Robust NAS under adversarial training: benchmark, theory, and beyond

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

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Residual-based attention in physics-informed neural networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

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

Aggregating Spatial and Photometric Context for Photometric Stereo

Robust machine learning for neuroscientific inference

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Robust NAS under adversarial training: benchmark, theory, and beyond

Residual-based attention in physics-informed neural networks

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

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks