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Alexander Semenenko

2 accepted papers

2026

Curse of Slicing: Why Sliced Mutual Information is a Deceptive Measure of Statistical Dependence

ICLR 2026poster

Sliced Mutual Information (SMI) is widely used as a scalable alternative to mutual information for measuring non-linear statistical dependence. Despite its advantages, such as faster convergence, robustness to high dimensionality, and nullification only under statistical independence, we demonstrate…

Cited by 1SourcecodeScholar
2025

Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax

ICLR 2025poster

Deep InfoMax (DIM) is a well-established method for self-supervised representation learning (SSRL) based on maximization of the mutual information between the input and the output of a deep neural network encoder. Despite the DIM and contrastive SSRL in general being well-explored, the task of learn…

Cited by 0SourcePDFScholar