NeuralEF: Deconstructing Kernels by Deep Neural Networks

NeuralEF: Deconstructing Kernels by Deep Neural Networks

30 April 2022

Jun Zhu

Papers citing "NeuralEF: Deconstructing Kernels by Deep Neural Networks"

12 / 12 papers shown

Title
Operator SVD with Neural Networks via Nested Low-Rank Approximation Jeonghun Ryu Xiangxiang Xu H. Erol Yuheng Bu Lizhong Zheng G. Wornell 21 0 0 06 Feb 2024
Improved Operator Learning by Orthogonal Attention Zipeng Xiao Zhongkai Hao Bokai Lin Zhijie Deng Hang Su 24 13 0 19 Oct 2023
Contrastive Learning as Kernel Approximation Konstantinos Christopher Tsiolis SSL 18 0 0 06 Sep 2023
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition Yash Chandak S. Thakoor Z. Guo Yunhao Tang Rémi Munos Will Dabney Diana Borsa 13 2 0 01 May 2023
Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation Zhijie Deng Yucen Luo 24 6 0 06 Apr 2023
Uni-Fusion: Universal Continuous Mapping Yijun Yuan Andreas Nüchter 40 9 0 22 Mar 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces Charline Le Lan Joshua Greaves Jesse Farebrother Mark Rowland Fabian Pedregosa Rishabh Agarwal Marc G. Bellemare 44 8 0 08 Dec 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 47 19 0 23 Oct 2022
Neural Eigenfunctions Are Structured Representation Learners Zhijie Deng Jiaxin Shi Hao Zhang Peng Cui Cewu Lu Jun Zhu 50 14 0 23 Oct 2022
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions Daniel D. Johnson Ayoub El Hanchi Chris J. Maddison SSL 24 22 0 04 Oct 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 30 4 0 15 May 2022
Spectral Inference Networks: Unifying Deep and Spectral Learning David Pfau Stig Petersen Ashish Agarwal David Barrett Kimberly L. Stachenfeld 51 40 0 06 Jun 2018