Learning about an exponential amount of conditional distributions

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Authors Yann LeCun, David Lopez-Paz, Maxime Oquab, Mohamed Ishmael Belghazi
Journal/Conference Name NeurIPS 2019 12
Paper Category
Paper Abstract We introduce the Neural Conditioner (NC), a self-supervised machine able to learn about all the conditional distributions of a random vector $X$. The NC is a function $NC(x \cdot a, a, r)$ that leverages adversarial training to match each conditional distribution $P(X_r|X_a=x_a)$. After training, the NC generalizes to sample from conditional distributions never seen, including the joint distribution. The NC is also able to auto-encode examples, providing data representations useful for downstream classification tasks. In sum, the NC integrates different self-supervised tasks (each being the estimation of a conditional distribution) and levels of supervision (partially observed data) seamlessly into a single learning experience.
Date of publication 2019
Code Programming Language Unspecified
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