Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
| Date | Stars |
|---|---|
| 2026-07-31 | 404 |
| 2026-08-06 | 404 |
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[](https://github.com/MinghuiChen43/awesome-deep-phenomena/graphs/commit-activity)   [](https://github.com/MinghuiChen43/awesome-deep-phenomena/stargazers) [](https://github.com/MinghuiChen43/awesome-deep-phenomena) [](https://github.com/MinghuiChen43/awesome-deep-phenomena/watchers) [](https://github.com/MinghuiChen43/awesome-deep-phenomena/network/members) # Awesome Deep Phenomena [](https://github.com/sindresorhus/awesome) Our understanding of modern neural networks lags behind their practical successes. This growing gap poses a challenge to the pace of progress in machine learning because fewer pillars of knowledge are available to designers of models and algorithms [(Hanie Sedghi)](https://odsc.com/speakers/understanding-deep-learning-phenomena/). Inspired by the [ICML 2019 workshop Identifying and Understanding Deep Learning Phenomena](http://deep-phenomena.org/), I collect papers and related resources which present interesting empirical study and insight into the nature of deep learning. # Table of Contents <img width="35%" align="right" alt="DALLE" src="img/DALL·E 2024-01-15 16.32.32 - A highly detailed digital art piece featuring an artificial brain integrated with advanced technology and physics concepts. The brain, depicted in the.png" /> - [Empirical Study](#empirical-study) - [Neural Collapse](#neural-collapse) - [Deep Double Descent](#deep-double-descent) - [Lottery Ticket Hypothesis](#lottery-ticket-hypothesis) - [Emergence and Phase Transitions](#emergence-and-phase-transitions) - [Interactions with Neuroscience](#interactions-with-neuroscience) - [Information Bottleneck](#information-bottleneck) - [Neural Tangent Kernel](#neural-tangent-kernel) - [Other Papers](#others) - [Resources](#related-resources) ## Empirical Study  ### Empirical Study: 2026 - Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization. [[paper]](https://arxiv.org/abs/2607.13432) - Jiaxuan Cheng. - Key Word: Neural Network Plasticity; Local Redundancy; Synthetic Memorization; Continual Learning; Transfer Learning. - <details><summary>Digest</summary> This paper introduces local redundancy, an information-theoretic measure of a neural network's ability to adapt to new tasks. Because the exact quantity is intractable, the author proves that the expected squared gradient norm on a synthetic memorization task provides an efficient lower bound. Experiments on continual image classification and time series transfer learning show that local redundancy predicts downstream performance better than effective rank, dead neuron fraction, and weight norm, and can select useful pretraining checkpoints after validation loss plateaus.</details> - Revisiting the Volume Hypothesis. [[paper]](https://arxiv.org/abs/2606.31282) - Ari Pakman, Lior Kreimer, Yakir Berchenko. - Key Word: Volume Hypothesis; Loss Landscape; Generalization; Stochastic Gradient Descent; Random Sampling. - <details><summary>Digest</summary> This paper revisits whether good-generalizing low-loss basins occupy more volume in weight space than poor-
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4fabde7eda8b2e9d, topic:awesome-list, desc:curated list