arXiv Machine Learning August 19, 2026 By James Hazelden, Eric Shea-Brown

Center-Manifold Reduction of Learning at Bifurcations: Interference and Rich Learning in Recurrent Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2605. 12763v2 Announce Type: replace Abstract: Rich learning in recurrent neural networks often proceeds through sudden transitions in latent dynamics, but there is little theory predicting how gradient descent behaves during these events.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

rag

arXiv Machine Learning

Jun 16

A Bifurcation Theory Framework for Gradient Descent on the Edge of Stability

arXiv:2606. 15551v1 Announce Type: new Abstract: The Edge of Stability (EoS) phenomenon, where gradient descent operates with sharpness exceeding the classical convergence threshold yet the loss decreases over long timescales, is ubiquitous in modern deep learning but remains poorly understood in realistic settings.

By Eric Gan

diffusion

Hugging Face Trending Papers

Jun 29

Scalar Representations of Neural Network Training Dynamics

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.

rag

arXiv Machine Learning

Aug 3

Embedding of Low-Dimensional Sensory Dynamics in Recurrent Networks: Implications for the Geometry of Neural Representation

arXiv:2601. 19019v3 Announce Type: replace-cross Abstract: Neural population activity in sensory cortex is organized on low-dimensional manifolds, but why such manifolds arise and what determines their geometry remain unclear.

By Vikas N. O'Reilly-Shah, Alessandro Maria Selvitella

rag

arXiv AI

Jun 10

Machine Learning Methods for Studying Latent Neural Activity Dynamics

arXiv:2606. 10530v1 Announce Type: cross Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons.

By Shufeng Kong, Fumei Deng, Xinyi Dong, Caihua Liu, Weiwei Chen, Yingheng Wang, Daniel Cao, Azahara Oliva, Antonio Fernandez-Ruiz, Carla Gomes

llms diffusion benchmarks

arXiv Machine Learning

Jul 3

Dendritic In-Context Learning in a Single-Layer Spiking Neural Network

arXiv:2607. 02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs.

By Juwei Shen, Yujie Wu, Changwen Chen

llms benchmarks

Related stories

Scalar Representations of Neural Network Training Dynamics

A Bifurcation Theory Framework for Gradient Descent on the Edge of Stability

Scalar Representations of Neural Network Training Dynamics

Embedding of Low-Dimensional Sensory Dynamics in Recurrent Networks: Implications for the Geometry of Neural Representation

Machine Learning Methods for Studying Latent Neural Activity Dynamics

Dendritic In-Context Learning in a Single-Layer Spiking Neural Network