Large Scale Structure of Neural Network Loss Landscapes

11 June 2019

Papers citing "Large Scale Structure of Neural Network Loss Landscapes"

24 / 24 papers shown

Title
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity Keyao Zhan Puheng Li Lei Wu MoMe 79 0 0 13 Mar 2025
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks Kaizheng Wang Keivan K1 Shariatmadar Shireen Kudukkil Manchingal Fabio Cuzzolin David Moens Hans Hallez UQCV BDL 92 12 0 28 Jan 2025
Reinforcement Teaching Alex Lewandowski Calarina Muslimani Dale Schuurmans Matthew E. Taylor Jun-Jie Luo 76 1 0 28 Jan 2025
Input Space Mode Connectivity in Deep Neural Networks Jakub Vrabel Ori Shem-Ur Yaron Oz David Krueger 48 1 0 09 Sep 2024
A survey of deep learning optimizers -- first and second order methods Rohan Kashyap ODL 29 6 0 28 Nov 2022
Multiple Modes for Continual Learning Siddhartha Datta N. Shadbolt CLL MoMe 41 2 0 29 Sep 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 44 22 0 22 Sep 2022
Linear Connectivity Reveals Generalization Strategies Jeevesh Juneja Rachit Bansal Kyunghyun Cho João Sedoc Naomi Saphra 232 45 0 24 May 2022
Interpolating Compressed Parameter Subspaces Siddhartha Datta N. Shadbolt 32 5 0 19 May 2022
Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks Siddhartha Datta N. Shadbolt AAML 21 6 0 07 Mar 2022
When Do Flat Minima Optimizers Work? Jean Kaddour Linqing Liu Ricardo M. A. Silva Matt J. Kusner ODL 11 58 0 01 Feb 2022
Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector Guoliang Lin Hanlu Chu Hanjiang Lai MoMe CLL 29 43 0 15 Oct 2021
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks R. Entezari Hanie Sedghi O. Saukh Behnam Neyshabur MoMe 35 215 0 12 Oct 2021
Connecting Low-Loss Subspace for Personalized Federated Learning S. Hahn Minwoo Jeong Junghye Lee FedML 14 18 0 16 Sep 2021
What can linear interpolation of neural network loss landscapes tell us? Tiffany J. Vlaar Jonathan Frankle MoMe 22 27 0 30 Jun 2021
Extracting Global Dynamics of Loss Landscape in Deep Learning Models Mohammed Eslami Hamed Eramian Marcio Gameiro W. Kalies Konstantin Mischaikow 16 1 0 14 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 0 14 May 2021
Learning Neural Network Subspaces Mitchell Wortsman Maxwell Horton Carlos Guestrin Ali Farhadi Mohammad Rastegari UQCV 16 85 0 20 Feb 2021
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them Chen Liu Mathieu Salzmann Tao R. Lin Ryota Tomioka Sabine Süsstrunk AAML 19 81 0 15 Jun 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 17 639 0 20 Feb 2020
Deep Ensembles: A Loss Landscape Perspective Stanislav Fort Huiyi Hu Balaji Lakshminarayanan OOD UQCV 13 617 0 05 Dec 2019
Stiffness: A New Perspective on Generalization in Neural Networks Stanislav Fort Pawel Krzysztof Nowak Stanislaw Jastrzebski S. Narayanan 19 94 0 28 Jan 2019
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 278 2,888 0 15 Sep 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 179 1,185 0 30 Nov 2014