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Word Embeddings through Hellinger PCA

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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

Word Embeddings through Hellinger PCA

Graph Chatbot

Chattez avec Graph Search

Network time series forecasting in photovoltaics power production

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

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Incorporating Projective Geometry into Deep Learning

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

Sparse autoregressive neural networks for classical spin systems

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

Robust machine learning for neuroscientific inference

Network time series forecasting in photovoltaics power production

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

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Incorporating Projective Geometry into Deep Learning

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

Sparse autoregressive neural networks for classical spin systems

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

Robust machine learning for neuroscientific inference