Understanding Deep Neural Networks via Linear Separability of Hidden Layers

26 July 2023

Papers citing "Understanding Deep Neural Networks via Linear Separability of Hidden Layers"

6 / 6 papers shown

Title
Geometric Properties and Graph-Based Optimization of Neural Networks: Addressing Non-Linearity, Dimensionality, and Scalability Michael Wienczkowski Addisu Desta Paschal Ugochukwu AI4CE GNN 41 0 0 24 Feb 2025
Embedded Visual Prompt Tuning Wenqiang Zu Shenghao Xie Qing Zhao Guoqi Li Lei Ma VLM MedIm 49 9 0 01 Jul 2024
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing Jason Alan Fries Leon Weber Natasha Seelam Gabriel Altay Debajyoti Datta ... Minh Chien Vu Trishala Neeraj Jonas Golde Albert Villanova del Moral Benjamin Beilharz LM&MA 95 46 0 30 Jun 2022
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes Alexander Kolesnikov André Susano Pinto Lucas Beyer Xiaohua Zhai Jeremiah Harmsen N. Houlsby 103 67 0 20 May 2022
FLEX: Unifying Evaluation for Few-Shot NLP Jonathan Bragg Arman Cohan Kyle Lo Iz Beltagy 202 104 0 15 Jul 2021
Norm-Based Capacity Control in Neural Networks Behnam Neyshabur Ryota Tomioka Nathan Srebro 119 577 0 27 Feb 2015