(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
[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️
analytic solution to the git-merge algorithm, derived from "Git Re-Basin: Merging Models modulo Permutation Symmetries"
[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
This project builds on recent research that explores the phenomenon of Grokking. The goal is to investigate when, why, and how grokking occurs, focusing on transformers under various batch sizes.
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."