arXiv Machine Learning July 3, 2026 By Preston Pitzer, Anish Pradhan, Harpreet S. Dhillon

Fourier Preconditioning for Neural Feature Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 02199v1 Announce Type: cross Abstract: Mutual information (MI)-inspired feature learning techniques are capable of generating low-dimensional embeddings that retain nonlinear dependence structures, but direct estimations of MI suffer from noisy probability distribution estimates in the low-data regime.

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

arXiv Machine Learning

Aug 10

Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features

arXiv:2510. 19399v2 Announce Type: replace Abstract: Spectral bias, the tendency of neural networks to learn low-frequency features first, is a well-known issue with many training algorithms for physics-informed neural networks (PINNs).

By Yulun Wu, Miguel Aguiar, Karl H. Johansson, Matthieu Barreau

diffusion benchmarks safety

arXiv Machine Learning

Aug 12

Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network

arXiv:2608. 10351v1 Announce Type: new Abstract: In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN).

By Karl Pierce, Yuehaw Khoo, Haizhao Yang

arXiv Machine Learning

Jun 16

The Information-Theoretic Benefit of Shared Representations under Orthogonality Constraints

arXiv:2606. 16028v1 Announce Type: new Abstract: Modern deep learning architectures are increasingly multi-task and multi-modal, using a pretrained foundation model combined with task-specific, fine-tuned models.

By Thomas Dittrich, Oliver Potocki, Philipp Grohs

fine-tuning multimodal

arXiv AI

Jul 7

Learning to Discover Iterative Spectral Algorithms

arXiv:2602. 09530v2 Announce Type: replace-cross Abstract: We introduce AutoSpec, a neural network framework for discovering iterative spectral algorithms for large-scale numerical linear algebra and numerical optimization.

By Zihang Liu, Oleg Balabanov, Yaoqing Yang, Michael W. Mahoney

arXiv Machine Learning

Jul 1

A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning

arXiv:2602. 20062v2 Announce Type: replace Abstract: Pretraining and fine-tuning are central stages in modern machine learning systems.

By Nicolas Anguita, Francesco Locatello, Andrew M. Saxe, Marco Mondelli, Flavia Mancini, Samuel Lippl, Clementine Domine

llms fine-tuning safety

arXiv Machine Learning

Jun 18

Regular Fourier Features for Nonstationary Gaussian Processes

arXiv:2602. 23006v2 Announce Type: replace-cross Abstract: Simulating a Gaussian process requires sampling from a high-dimensional Gaussian distribution, which scales cubically with the number of sample locations.

By Arsalan Jawaid, Abdullah Karatas, J\"org Seewig

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Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features

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The Information-Theoretic Benefit of Shared Representations under Orthogonality Constraints

Learning to Discover Iterative Spectral Algorithms

A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning

Regular Fourier Features for Nonstationary Gaussian Processes