Publication

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Graph Chatbot

Chat with Graph Search

Disentangling feature and lazy training in deep neural networks

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

The Unstoppable Rise of Computational Linguistics in Deep Learning

Learning stereo reconstruction with deep neural networks

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Scaling description of generalization with number of parameters in deep learning

Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

Learning approaches to high-fidelity optical diffraction tomography