Search Results for author: Mohamed Ishmael Belghazi

Found 6 papers, 3 papers with code

What classifiers know what they don't know?

no code implementations29 Sep 2021 Mohamed Ishmael Belghazi, David Lopez-Paz

Adding new datasets, algorithms, measures, or metrics is a matter of a few lines of code-in so hoping that UIMNET becomes a stepping stone towards realistic, rigorous, and reproducible research in uncertainty estimation.

Decision Making

What classifiers know what they don't?

1 code implementation13 Jul 2021 Mohamed Ishmael Belghazi, David Lopez-Paz

Adding new datasets, algorithms, measures, or metrics is a matter of a few lines of code-in so hoping that UIMNET becomes a stepping stone towards realistic, rigorous, and reproducible research in uncertainty estimation.

Decision Making

Learning about an exponential amount of conditional distributions

1 code implementation NeurIPS 2019 Mohamed Ishmael Belghazi, Maxime Oquab, Yann Lecun, David Lopez-Paz

We introduce the Neural Conditioner (NC), a self-supervised machine able to learn about all the conditional distributions of a random vector $X$.

General Classification

Mutual Information Neural Estimation

no code implementations ICML 2018 Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, Devon Hjelm

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.

General Classification

Hierarchical Adversarially Learned Inference

no code implementations ICLR 2018 Mohamed Ishmael Belghazi, Sai Rajeswar, Olivier Mastropietro, Negar Rostamzadeh, Jovana Mitrovic, Aaron Courville

We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model.

MINE: Mutual Information Neural Estimation

18 code implementations12 Jan 2018 Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, R. Devon Hjelm

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.

General Classification

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