Deep neural networks architectures from the perspective of manifold learning

6 June 2023

Papers citing "Deep neural networks architectures from the perspective of manifold learning"

9 / 9 papers shown

Title
Identifying Key Challenges of Hardness-Based Resampling Pawel Pukowski Venet Osmani 36 0 0 09 Apr 2025
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data Jiaojiao Zhang Jiang Hu Anthony Man-Cho So Mikael Johansson 37 2 0 12 Jun 2024
Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold Anupam Chaudhuri Anj Simmons Mohamed Abdelrazek 23 0 0 08 Mar 2024
AI-generated text boundary detection with RoFT Laida Kushnareva T. Gaintseva German Magai S. Barannikov Dmitry Abulkhanov Kristian Kuznetsov Eduard Tulchinskii Irina Piontkovskaya Sergey I. Nikolenko DeLMO 33 5 0 14 Nov 2023
On original and latent space connectivity in deep neural networks Boyang Gu Anastasia Borovykh GNN 3DPC 32 1 0 12 Nov 2023
Patches Are All You Need? Asher Trockman J. Zico Kolter ViT 225 402 0 24 Jan 2022
The Intrinsic Dimension of Images and Its Impact on Learning Phillip E. Pope Chen Zhu Ahmed Abdelkader Micah Goldblum Tom Goldstein 197 261 0 18 Apr 2021
Deep Networks and the Multiple Manifold Problem Sam Buchanan D. Gilboa John N. Wright 166 39 0 25 Aug 2020
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations Yuanhan Zhang Zhen-fei Yin Yidong Li Guojun Yin Junjie Yan Jing Shao Ziwei Liu CVBM 54 162 0 24 Jul 2020