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Found 5 papers, 1 papers with code
Lazy FSCA for Unsupervised Variable Selection
In this paper a "lazy" implementation of the FSCA algorithm (L-FSCA) is proposed, which, although not equivalent to FSCA due to the absence of submodularity, has the potential to yield comparable performance while being up to an order of magnitude faster to compute.
$β$-Variational Classifiers Under Attack
Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision.
IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces
Learning useful representations of complex data has been the subject of extensive research for many years.
Proximal Deterministic Policy Gradient
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms.
Adversarial Training Reduces Information and Improves Transferability
Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility.
