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ORCID1st NeurReps 2022, New Orleans, Lousiana, USA
- Sophia Sanborn, Christian Shewmake, Simone Azeglio, Arianna Di Bernardo, Nina Miolane:
NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 03 December 2022, New Orleans, Lousiana, USA. Proceedings of Machine Learning Research 197, PMLR 2022 - Sophia Sanborn, Christian Shewmake, Simone Azeglio, Arianna Di Bernardo, Nina Miolane:
Preface. i-vi - Noah Shutty, Casimir Wierzynski:
Computing representations for Lie algebraic networks. 1-21 - Ho Yin Chau, Frank Qiu, Yubei Chen, Bruno A. Olshausen:
Disentangling images with Lie group transformations and sparse coding. 22-47 - Tycho F. A. van der Ouderaa, Mark van der Wilk:
Sparse Convolutions on Lie Groups. 48-62 - David Klee, Ondrej Biza, Robert Platt, Robin Walters:
Image to Icosahedral Projection for SO(3) Object Reasoning from Single-View Images. 64-80 - Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero, Jesús Angulo:
Moving frame net: SE(3)-equivariant network for volumes. 81-97 - David A. R. Robin, Kevin Scaman, Marc Lelarge:
Periodic signal recovery with regularized sine neural networks. 98-110 - Anshul Thakur, Vinayak Abrol, Pulkit Sharma:
Does Geometric Structure in Convolutional Filter Space Provide Filter Redundancy Information? 111-121 - Sarah McGuire, Shane Jackson, Tegan Emerson, Henry Kvinge:
Do neural networks trained with topological features learn different internal representations? 122-136 - Thomas Davies, Jack Aspinall, Bryan Wilder, Tran-Thanh Long:
Fuzzy c-means clustering in persistence diagram space for deep learning model selection. 137-157 - Arif Dönmez:
On the ambiguity in classification. 158-170 - Danil Akhtiamov, Matt Thomson:
Connectedness of loss landscapes via the lens of Morse theory. 171-181 - Benjamin Aslan, Daniel Platt, David Sheard
:
Group invariant machine learning by fundamental domain projections. 181-218 - Yu Tian, Zachary Lubberts, Melanie Weber:
Mixed-membership community detection via line graph curvature. 219-233 - Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig:
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles. 234-257 - John J. Vastola, Zach Cohen, Jan Drugowitsch:
Is the information geometry of probabilistic population codes learnable? 258-277 - Binxu Wang, Carlos R. Ponce:
On the level sets and invariance of neural tuning landscapes. 278-300 - Luca Baroni, Mohammad Bashiri, Konstantin F. Willeke, Ján Antolík, Fabian H. Sinz:
Learning invariance manifolds of visual sensory neurons. 301-326 - Ramakrishnan Iyer, Joshua H. Siegle, Gayathri Mahalingam, Shawn R. Olsen, Stefan Mihalas:
Geometry of inter-areal interactions in mouse visual cortex. 327-353 - David A. Klindt, Sigurd Gaukstad, Melvin Vaupel, Erik Hermansen, Benjamin A. Dunn:
Topological ensemble detection with differentiable yoking. 354-369 - Dehong Xu, Ruiqi Gao, Wenhao Zhang, Xue-Xin Wei, Ying Nian Wu:
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells. 370-387 - Sunny Duan, Mikail Khona, Adrian Bertagnoli, Sarthak Chandra, Ila Fiete:
See and Copy: Generation of complex compositional movements from modular and geometric RNN representations. 388-400
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