arXiv Machine Learning July 1, 2026 By Robin Theriault

Explaining Machine Learning and Memorization with Statistical Mechanics

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arXiv:2606. 31110v1 Announce Type: new Abstract: Artificial neural networks (NNs) and machine learning (ML) algorithms are poorly understood from a theoretical perspective, which makes it difficult to fully realize their potential and overcome their weaknesses.

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arXiv Machine Learning

Jun 8

Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory

arXiv:2606. 06624v1 Announce Type: new Abstract: In the current era of deep learning and especially generative models, there is significant investment in training very large generative models.

By San Buchanan, Druv Pai, Peng Wang, Yi Ma

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arXiv Machine Learning

Sep 10

The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networks

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arXiv Machine Learning

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Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines

arXiv:2605. 19178v2 Announce Type: replace-cross Abstract: The great success of neural networks primarily arises from the presence of the large number of weight parameters combined with nonlinearities in the input-output relationship of single neurons.

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training

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arXiv Machine Learning

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Geometric origin of adversarial vulnerability in deep learning

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arXiv Machine Learning

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A Dynamical Systems Perspective on the Analysis of Neural Networks

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