{"id":"arxiv:2405.01012","title":"Correcting Biased Centered Kernel Alignment Measures in Biological and Artificial Neural Networks","date":"2024-05-02","section":"Representation alignment","quality":-0.505307,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/","source_url":"https://arxiv.org/abs/2405.01012","text_available":true,"authors":["Alex Murphy","Joel Zylberberg","Alona Fyshe"],"outgoing":[],"incoming":[{"source":"arxiv:2502.15104","target":"arxiv:2405.01012","evidence":"id","id":"arxiv:2502.15104","title":"Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Features","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/bb284056f9389cbe75ad82bc3d85b997ce69212d23f95de2baf599ad64d97618/"},{"source":"arxiv:2602.14486","target":"arxiv:2405.01012","evidence":"id","id":"arxiv:2602.14486","title":"Revisiting the Platonic Representation Hypothesis: An 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Networks","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"Abstract","offset":101,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"1 Introduction","offset":2029,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"2 CKA Overview","offset":6222,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"2.1 Mathematical Formulation & Properties","offset":6241,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"2.2 Criticisms","offset":9178,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"2.3 Applications to Neuroscience","offset":11859,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"3 Methods","offset":14617,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"3.1 Brain Representations","offset":14631,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-1.html#text"},{"title":"3.2 Model Representations","offset":16508,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"4 CKA Sensitivity to Random Data","offset":17962,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"5 CKA Sensitivity to Stimuli-Driven Neural Responses","offset":20230,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"6 CKA Sensitivity to Shuffled Data","offset":23066,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"7 Conclusion","offset":28114,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"Acknowledgments","offset":29917,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"Appendix A Appendix","offset":30045,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"A.1 Generating Random Data","offset":30069,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-2.html#text"},{"title":"A.2 MEG Preprocessing & Sensor Selection","offset":30951,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-3.html#text"},{"title":"A.3 Code Implementation","offset":32071,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-3.html#text"},{"title":"A.4 Per-Participant Functional MRI ROI Sizes","offset":32406,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-3.html#text"},{"title":"A.5 Unbiased HSIC Kernel-Centering Formula","offset":32456,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-3.html#text"},{"title":"A.6 Results for CKA Sensitivity to Shuffled Data (CORnet-S)","offset":33182,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-3.html#text"},{"title":"A.7 Results for CKA Sensitivity to Partially Shuffled Data (ResNet18)","offset":33247,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/a961d84a4ef2fcab54a7b1f632d950182fa2353e0c4d8a0107320f996f943d35/part-3.html#text"},{"title":"Instructions for reporting 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