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Found 6 papers, 3 papers with code
What classifiers know what they don't know?
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.
What classifiers know what they don't?
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.
Learning about an exponential amount of conditional distributions
We introduce the Neural Conditioner (NC), a self-supervised machine able to learn about all the conditional distributions of a random vector $X$.
Mutual Information Neural Estimation
We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.
Hierarchical Adversarially Learned Inference
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model.
MINE: Mutual Information Neural Estimation
We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.
