Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity

3 November 2021

Papers citing "Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity"

6 / 6 papers shown

Title
Your contrastive learning problem is secretly a distribution alignment problem Zihao Chen Chi-Heng Lin Ran Liu Jingyun Xiao Eva L. Dyer 135 1 0 27 Feb 2025
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective Chi-Heng Lin Chiraag Kaushik Eva L. Dyer Vidya Muthukumar 74 31 0 10 Oct 2022
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes A. Fedorov Eloy P. T. Geenjaar Lei Wu Tristan Sylvain T. DeRamus Margaux Luck Maria B. Misiura R. Devon Hjelm Sergey Plis Vince D. Calhoun 31 3 0 07 Sep 2022
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles Justin Jude M. Perich L. Miller Matthias H Hennig 49 3 0 19 May 2022
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity Felix Pei Joel Ye D. Zoltowski Anqi Wu Raeed H. Chowdhury ... L. Miller Jonathan W. Pillow Il Memming Park Eva L. Dyer C. Pandarinath 296 90 0 09 Sep 2021
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments Mathilde Caron Ishan Misra Julien Mairal Priya Goyal Piotr Bojanowski Armand Joulin OCL SSL 318 4,107 0 17 Jun 2020