Visualize loss landscape
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Updated
Jun 19, 2020 - Julia
Visualize loss landscape
Surrogate Gap Guided Sharpness-Aware Minimization (GSAM) implementation for keras/tensorflow 2
[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"
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。
[TMLR] "Can You Win Everything with Lottery Ticket?" by Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang
This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Networks(PINNs)"
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.
Create animations for the optimization trajectory of neural nets
(ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?"
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