arXiv Machine Learning By Jin Lei

Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing

Read the original on arXiv Machine Learning →

arXiv:2606. 09950v1 Announce Type: new Abstract: Averaging a neural network over its random parameters and marginalizing a Gaussian sector are the same operation, the Schur complement of the eliminated block, and when that block is closed it returns a covariance and its inverse.

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

arXiv Machine Learning
Jun 19

Effective Dimension Governs Generalization in Quantum Kernel Vision Models

arXiv:2606. 20183v1 Announce Type: new Abstract: Recent quantum vision models-quantum vision transformers and quantum convolutional networks-report two striking but unexplained empirical phenomena: (i) ansatze with more, or more uniformly distributed, entanglement generalize better, and (ii) injecting quantum noise can improve test accuracy rather than degrade it.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao
arXiv Machine Learning
Jul 16

Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations

arXiv:2607. 13749v1 Announce Type: new Abstract: Neural networks trained on modular arithmetic exhibit grokking, a delayed transition from memorisation to generalisation known to depend on model capacity: too little and the network memorises slowly or not at all, too much and it generalises almost immediately.

By Chon-Fai Kam, Xavier Cadet, Miloud Bessafi, Frederic Cadet
arXiv AI
Jul 29

A Path Integral Model of Cognition

arXiv:2607. 24807v1 Announce Type: cross Abstract: We develop the mathematical and physical formulation of cognitive cost optimization that underlies the path-integral model of consciousness.

By Haruki Emori, Kazunori Kondo, Atsushi Iriki, Andrei Khrennikov