From d9db15b28773546279e2da1e8a15804134d843ab Mon Sep 17 00:00:00 2001 From: cloudaen Date: Wed, 20 May 2026 13:38:20 -0600 Subject: [PATCH] sub-02: add missing citations and fix set-notation rendering - Add bib entries for wightman2019timm, deng2009imagenet, nguyen2021wide (referenced in body but previously unresolved). - Fix MathJax set-notation in Methodology > Spectral rounding: braces inside $...$ were treated as grouping; escape them so the set is visible in the render. --- .../assets/bibliography/2026-04-27-sub-02.bib | 23 +++++++++++++++++++ 2026/challenge/blog/sub-02/index.html | 2 +- 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/2026/challenge/assets/bibliography/2026-04-27-sub-02.bib b/2026/challenge/assets/bibliography/2026-04-27-sub-02.bib index 43dbfad..c21ba9c 100644 --- a/2026/challenge/assets/bibliography/2026-04-27-sub-02.bib +++ b/2026/challenge/assets/bibliography/2026-04-27-sub-02.bib @@ -107,4 +107,27 @@ @inproceedings{ding2021grounding booktitle={Advances in Neural Information Processing Systems}, volume={34}, year={2021} +} + +@misc{wightman2019timm, + title={PyTorch Image Models}, + author={Wightman, R.}, + year={2019}, + publisher={GitHub}, + howpublished={\url{https://github.com/rwightman/pytorch-image-models}}, + doi={10.5281/zenodo.4414861} +} + +@inproceedings{deng2009imagenet, + title={{ImageNet}: A Large-Scale Hierarchical Image Database}, + author={Deng, J. and Dong, W. and Socher, R. and Li, L.-J. and Li, K. and Fei-Fei, L.}, + booktitle={CVPR 2009}, + year={2009} +} + +@article{nguyen2021wide, + title={Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth}, + author={Nguyen, T. and Raghu, M. and Kornblith, S.}, + journal={ICLR 2021}, + year={2021} } \ No newline at end of file diff --git a/2026/challenge/blog/sub-02/index.html b/2026/challenge/blog/sub-02/index.html index 7192071..b273a8b 100644 --- a/2026/challenge/blog/sub-02/index.html +++ b/2026/challenge/blog/sub-02/index.html @@ -317,7 +317,7 @@

The optimizer ensemble

  1. -Spectral rounding: Extracted leading eigenvectors of the CKA matrix $S$ and performed randomized rounding over linear combinations of the top-$r$ eigenspaces (1,000+ random projections across $r \in {1, 2, 3, 5, 10, 20, 30, 50}$).
  2. +Spectral rounding: Extracted leading eigenvectors of the CKA matrix $S$ and performed randomized rounding over linear combinations of the top-$r$ eigenspaces (1,000+ random projections across $r \in \{1, 2, 3, 5, 10, 20, 30, 50\}$).
  3. Greedy search: Seeded with the globally highest-CKA pair, iteratively adding the model that maximized marginal gain in total CKA.