no code implementations • 29 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.
1 code implementation • 13 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.
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 $X$.
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.
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.
21 code implementations • 12 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.