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Found 6 papers, 4 papers with code
Indirectly Parameterized Concrete Autoencoders
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly.
Logistic-Normal Likelihoods for Heteroscedastic Label Noise
A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters can be learned by minimizing the negative log-likelihood.
Deep Double Descent via Smooth Interpolation
The ability of overparameterized deep networks to interpolate noisy data, while at the same time showing good generalization performance, has been recently characterized in terms of the double descent curve for the test error.
Consistency Regularization Can Improve Robustness to Label Noise
Consistency regularization is a commonly-used technique for semi-supervised and self-supervised learning.
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Prior works have found it beneficial to combine provably noise-robust loss functions e. g., mean absolute error (MAE) with standard categorical loss function e. g. cross entropy (CE) to improve their learnability.
Ranked #17 on
Image Classification
on mini WebVision 1.0
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation
In this work we aim to obtain computationally-efficient uncertainty estimates with deep networks.
