Hugging Face Trending Papers June 29, 2026

Scalar Representations of Neural Network Training Dynamics

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Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape. However, the large number of trainable parameters makes the direct analysis of these dynamics challenging.

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arXiv Machine Learning

Jun 30

Scalar Representations of Neural Network Training Dynamics

arXiv:2606. 30384v1 Announce Type: new Abstract: Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape.

By Pedro Jim\'enez-Gonz\'alez, Miguel C. Soriano, Lucas Lacasa

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arXiv AI

3d ago

Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability

arXiv:2610.03000v1 Announce Type: cross Abstract: Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons (MLPs) require dynamic re-training with...

By Ambarish Moharil

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arXiv Machine Learning

Jun 17

A Dynamical Systems Perspective on the Analysis of Neural Networks

arXiv:2507. 05164v2 Announce Type: replace-cross Abstract: In this chapter, we utilize dynamical systems to analyze several aspects of machine learning algorithms.

By Dennis Chemnitz, Maximilian Engel, Christian Kuehn, Sara-Viola Kuntz

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arXiv Machine Learning

Jun 4

When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks

arXiv:2606. 04476v1 Announce Type: new Abstract: In this paper, we study the gradient descent dynamics for jointly training both layers of a one-hidden-layer ReLU network to fit a linear target function.

By Berk Tinaz, Changzhi Xie, Mahdi Soltanolkotabi

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arXiv Machine Learning

Jun 5

Gradient descent at the Edge of Stability: free energy model and kinetic description of the two-layer network

arXiv:2606. 05326v1 Announce Type: cross Abstract: We study the dynamics of gradient descent in the Edge of Stability regime, where the learning rate is large enough to induce persistent oscillations in the loss and the sharpness.

By Antonin Chodron de Courcel

arXiv Machine Learning

Jun 4

Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks

arXiv:2602. 14885v2 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical results, such as Hopfield's model of associative memory, rely on symmetric connectivity that restricts network dynamics to gradient-like flows.

By Ram\'on Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely

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Scalar Representations of Neural Network Training Dynamics

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