Fourier Feature Embeddings

Follow this topic Ask about this topic

Momentum

1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 16

All topics

CAFE+FNO: Fourier Kernel Generation via Multiplicative Feature Composition
Oct 7, 2026Hyungjoon Juen, Minwoo ShinFourier Neural Operator PDE Operator Learning
Department of Software, Yonsei University (Mirae Campus), Wonju 26493, Republic of Korea
Low-Dimensional Embeddings for Gaussian Kernels on Manifolds
Sep 14, 2026Soumik Dutta, Kunal DuttaFourier Feature Embeddings Kernel Methods
Institute of Informatics, University of Warsaw, Poland
ClockRoPE: Random Fourier Rotations for Temporal Routine Modeling
Jul 29, 2026Yiwen Chen, Joshua Ainslie, Krzysztof Choromanski +4Fourier Feature Embeddings Rotary Positional Embeddings
YouTube · Google DeepMind
Recovering Sharp Conductivity Features in the Finite-Data Calderón Problem with Physics-Informed Neural Networks
Jun 26, 2026Ali AlHadi Kalout, Pablo Tejerina-Pérez, Konstantin Karchev +5Fourier Feature Embeddings Image Reconstruction
Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Martí i Franquès 1, ES-08028, Barcelona, Spain · Departament de Física Quàntica i Astrofísica, Universitat de Barcelona, Martí i Franquès 1, ES-08028, Barcelona, Spain · ICREA, Pg. Lluis Companys 23, 08010, Barcelona, Spain +2
Flexformer: Flexible Linear Transformer with Learnable Attention Kernel
Jun 26, 2026Haoran Zhang, Feng ZhouFourier Feature Embeddings Long-Context Language Modeling
Center for Applied Statistics and School of Statistics, Renmin University of China Beijing, China
Prime Fourier Embeddings: A Principled Basis for Modular Arithmetic
Jun 22, 2026Hyunsang Hwang, Suhyun Bae, Donghun LeeFourier Feature Embeddings Representation Learning
Department of Mathematics, Korea University, Seoul, South Korea.
Fourier Features Let Agents Learn High Precision Policies with Imitation Learning
Jun 10, 2026Balázs Gyenes, Emiliyan Gospodinov, Jan Frieling +5Fourier Feature Embeddings Robot Skill Learning
Autonomous Learning Robots, Karlsruhe Institute of Technology, Germany · HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
Bernstein-Schur Kernels: Random Features by Sketched Modulation and Radial Randomization
Jun 8, 2026Taha BouhsineFourier Feature Embeddings Kernel Ridge Regression
Azetta AI
Fourier Feature Pyramids for Physics-Informed Neural Networks
May 22, 2026Brandon Zhao, Yixuan Wang, Jonathan T. Barron +3Fourier Feature Embeddings PDE Solving
Department of Computing and Mathematical Sciences, California Institute of Technology · Google DeepMind · Cahill Center for Astronomy and Astrophysics, California Institute of Technology
On periodic distributed representations using Fourier embeddings
May 11, 2026Jakeb ChouinardFourier Feature Embeddings Kernel Methods
Department of Systems Design Engineering, University of Waterloo · Centre for Theoretical Neuroscience, University of Waterloo
Neurally-plausible radial basis kernels using distributed Fourier embeddings
May 8, 2026Jakeb ChouinardFourier Feature Embeddings Kernel Methods
Department of Systems Design Engineering, University of Waterloo · Centre for Theoretical Neuroscience, University of Waterloo
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
May 7, 2026Joseph D. RamseyFourier Feature Embeddings Causal Discovery
Department of Philosophy Carnegie Mellon University Pittsburgh, PA 15213
Convergent Evolution: How Different Language Models Learn Similar Number Representations
Apr 22, 2026Deqing Fu, Tianyi Zhou, Mikhail Belkin +2Fourier Feature Embeddings Frequency-Domain Feature Learning
University of Southern California · UC San Diego
Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results
Mar 3, 2026Alberto Miño Calero, Luis Salamanca, Konstantinos E. TatsisFourier Feature Embeddings Neural Network Interpretability
Department of Engineering Cybernetics NTNU, Trondheim, Norway · Swiss Data Science Center (SDSC) ETH Zürich, Zürich, Switzerland
Regular Fourier Features for Nonstationary Gaussian Processes
Feb 26, 2026Arsalan Jawaid, Abdullah Karatas, Jörg SeewigFourier Feature Embeddings Gaussian Processes
Institute of Measurement and Sensor Technology, University of Kaiserslautern-Landau, Kaiserslautern, Germany · Independent Researcher, Mannheim, Germany
Fourier Multi-Component and Multi-Layer Neural Networks: Unlocking High-Frequency Potential
Feb 26, 2025Shijun Zhang, Hongkai Zhao, Yimin Zhong +1Fourier Feature Embeddings Neural Network Optimization
Department of Applied Mathematics Hong Kong Polytechnic University · Department of Mathematics Duke University · Department of Mathematics and Statistics Auburn University +1

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

CAFE+FNO: Fourier Kernel Generation via Multiplicative Feature Composition

Hyungjoon Juen, Minwoo Shin

Department of Software, Yonsei University (Mirae Campus), Wonju 26493, Republic of Korea

Fourier Neural Operator PDE Operator Learning Fourier Feature Embeddings Compositional Representation Learning

Open paper AskPDF arXiv

The Fourier Neural Operator (FNO) learns solution operators of partial differential equations (PDEs) through Fourier-space kernel parameterization, but frequency truncation can limit the learning of high-frequency variations. AM-FNO and SirenFNO generate kernels for all grid modes from spectral coordinates using shared networks, making coordinate encoding and generator design important. Recent work on implicit neural representations (INRs) has proposed constructing frequency interactions through explicit feature composition rather than relying on subsequent MLPs to form them implicitly. Building on this approach, we propose CAFE+FNO, which incorporates Content-Aware Frequency Encoding+ (CAFE+) into Fourier kernel generation. CAFE+ combines Fourier--Chebyshev features through parallel affine branches and a Hadamard product, forming interactions within and across the two feature families. A kernel MLP maps the resulting representation of each normalized spectral coordinate to a complex channel-mixing matrix. Each layer shares its generator across all stored modes, making the number of trainable parameters independent of the number of modes for a fixed architecture. We compare CAFE+FNO with existing FNO variants on five PDE benchmarks and conduct ablation studies on basis configuration, multiplicative composition, and bandwidth learnability. Code and experimental configurations are available at https://github.com/fabsk101/CAFEPlusFNO.git.

Momentum

Related topics

Latest papers 16