Publication

Masksembles for Uncertainty Estimation

Related publications (54)

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.

Masksembles for Uncertainty Estimation

Graph Chatbot

Chat with Graph Search

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Deep Learning Generalization with Limited and Noisy Labels

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Predicting nonlinear optical scattering with physics-driven neural networks

Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

On the robustness of randomized classifiers to adversarial examples

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Improving the Training of Compact Neural Networks for Visual Recognition

Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Deep Learning Generalization with Limited and Noisy Labels

Predicting nonlinear optical scattering with physics-driven neural networks

Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Regularization of polynomial networks for image recognition

On the robustness of randomized classifiers to adversarial examples

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Improving the Training of Compact Neural Networks for Visual Recognition

Regularization of polynomial networks for image recognition

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design