Publication

Deep Learning Works in Practice. But Does it Work in Theory?

Related publications (109)

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.

Deep Learning Works in Practice. But Does it Work in Theory?

Graph Chatbot

Chat with Graph Search

Multi-agent Reinforcement Learning for Assembly of a Spanning Structure

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

Fixing the problems of deep neural networks will require better training data and learning algorithms

BLACK-BOX ATTACKS ON IMAGE ACTIVITY PREDICTION AND ITS NATURAL LANGUAGE EXPLANATIONS

Confidence Matters: Applications to Semantic Segmentation

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Backpropagation-free training of deep physical neural networks

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Monotonicity Reasoning in the Age of Neural Foundation Models

XTab: Cross-table Pretraining for Tabular Transformers