Deep Neural Network Approximation of Invariant Functions through Dynamical Systems

18 August 2022

Papers citing "Deep Neural Network Approximation of Invariant Functions through Dynamical Systems"

8 / 8 papers shown

Title
Learning multi-modal generative models with permutation-invariant encoders and tighter variational bounds Marcel Hirt Domenico Campolo Victoria Leong Juan-Pablo Ortega DRL 8 0 0 01 Sep 2023
A Brief Survey on the Approximation Theory for Sequence Modelling Hao Jiang Qianxiao Li Zhong Li Shida Wang AI4TS 28 12 0 27 Feb 2023
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network Shijun Zhang Jianfeng Lu Hongkai Zhao CoGe 23 4 0 29 Jan 2023
On the Universal Approximation Property of Deep Fully Convolutional Neural Networks Ting-Wei Lin Zuowei Shen Qianxiao Li 24 4 0 25 Nov 2022
On the Approximation and Complexity of Deep Neural Networks to Invariant Functions Gao Zhang Jin-Hui Wu Shao-Qun Zhang 11 0 0 27 Oct 2022
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 101 115 0 28 Feb 2021
Universal Equivariant Multilayer Perceptrons Siamak Ravanbakhsh 98 48 0 07 Feb 2020
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 222 14,099 0 02 Dec 2016