sub-02: add missing citations and fix set-notation rendering

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}
 }

2026/challenge/blog/sub-02/index.html

@@ -317,7 +317,7 @@ <h3 id="the-optimizer-ensemble">The optimizer ensemble</h3>
 
 <ol>
   <li>
-<strong>Spectral rounding</strong>: 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 $r \in {1, 2, 3, 5, 10, 20, 30, 50}$).</li>
+<strong>Spectral rounding</strong>: 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 $r \in \{1, 2, 3, 5, 10, 20, 30, 50\}$).</li>
   <li>
 <strong>Greedy search</strong>: Seeded with the globally highest-CKA pair, iteratively adding the model that maximized marginal gain in total CKA.</li>
   <li>