(ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?"
-
Updated
Jul 14, 2022 - Python
(ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?"
Create animations for the optimization trajectory of neural nets
Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan.
Implements sharpness-aware minimization (https://arxiv.org/abs/2010.01412) in TensorFlow 2.
This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Networks(PINNs)"
[TMLR] "Can You Win Everything with Lottery Ticket?" by Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang
analytic solution to the git-merge algorithm, derived from "Git Re-Basin: Merging Models modulo Permutation Symmetries"
[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️
[Int. J. Comput. Vis. 2024] Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
Worth-reading papers and related awesome resources on deep learning optimization algorithms. 值得一读的深度学习优化器论文与相关资源。
Surrogate Gap Guided Sharpness-Aware Minimization (GSAM) implementation for keras/tensorflow 2
Visualize loss landscape
Code for NeurIPS 2024 paper "Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?"
Add a description, image, and links to the loss-landscape topic page so that developers can more easily learn about it.
To associate your repository with the loss-landscape topic, visit your repo's landing page and select "manage topics."