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The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models

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Publications associées à The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models | EPFL Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models

Graph Chatbot

Chattez avec Graph Search

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

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

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

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

Aggregating Spatial and Photometric Context for Photometric Stereo

Machine Learning for Modeling Stock Returns

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

Machine Learning for Modeling Stock Returns

Aggregating Spatial and Photometric Context for Photometric Stereo

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

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

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime