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A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
The HSIC Bottleneck: Deep Learning without Back-Propagation
TensorFlow implementation of Barlow Twins (https://arxiv.org/abs/2103.03230).
Pytorch Implementation of the Nonlinear Information Bottleneck
[ICML 2025] Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021
Project for the Large Scale Optimization course at Skoltech
[ICMLW 2023, IEEE CISS 2024] Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Implementation of the paper "MERINA+: Improving Generalization for Neural Video Adaptation via Information-Theoretic Meta-Reinforcement Learning" - N. Kan, et. al., 2023
The L0-SIGN implementation.
Deep Learning model training visualized using mutual information between the input and the output