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Bernd Illing
Bernd Illing
Unknown affiliation
Verified email at epfl.ch
Title
Cited by
Cited by
Year
Mermin–Wagner fluctuations in 2D amorphous solids
B Illing, S Fritschi, H Kaiser, CL Klix, G Maret, P Keim
Proceedings of the National Academy of Sciences 114 (8), 1856-1861, 2017
1602017
Biologically plausible deep learning—but how far can we go with shallow networks?
B Illing, W Gerstner, J Brea
Neural Networks 118, 90-101, 2019
1142019
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
B Illing, J Ventura, G Bellec, W Gerstner
Thirty-Fifth Conference on Neural Information Processing Systems, 2021, 2021
622021
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
J Brea, B Simsek, B Illing, W Gerstner
arXiv preprint arXiv:1907.02911, 2019
482019
Strain pattern in supercooled liquids
B Illing, S Fritschi, D Hajnal, C Klix, P Keim, M Fuchs
Physical review letters 117 (20), 208002, 2016
402016
NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways
WAM Wybo, MC Tsai, VAK Tran, B Illing, J Jordan, A Morrison, W Senn
Proceedings of the National Academy of Sciences 120 (32), e2300558120, 2023
62023
Dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways
WAM Wybo, MC Tsai, VA Khoa Tran, B Illing, J Jordan, A Morrison, ...
bioRxiv, 2022.11. 25.517941, 2022
22022
Biologically plausible unsupervised learning in shallow and deep neural networks
BA Illing
EPFL, 2021
2021
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