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Using Animal Motion Capture to Learn Neural Representations

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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

Using Animal Motion Capture to Learn Neural Representations

Graph Chatbot

Chattez avec Graph Search

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

Fast adaptation to rule switching using neuronal surprise

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

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

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

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

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Fast adaptation to rule switching using neuronal surprise

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

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

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

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

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

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Deep Learning Theory Through the Lens of Diagonal Linear Networks