Publication

All-memristive neuromorphic computing with level-tuned neurons

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec 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

All-memristive neuromorphic computing with level-tuned neurons

Graph Chatbot

Chattez avec Graph Search

Fast adaptation to rule switching using neuronal surprise

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

Robust machine learning for neuroscientific inference

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

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

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Topics in statistical physics of high-dimensional machine learning

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

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

Fast adaptation to rule switching using neuronal surprise

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

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

Topics in statistical physics of high-dimensional machine learning

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Robust machine learning for neuroscientific inference

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

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks