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ORCID2nd NeurReps 2023: New Orleans, Lousiana, USA
- Sophia Sanborn, Christian Shewmake, Simone Azeglio, Nina Miolane:
NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 16 December 2023, New Orleans, Lousiana, USA. Proceedings of Machine Learning Research 228, PMLR 2023 - Sophia Sanborn, Christian Shewmake, Simone Azeglio, Nina Miolane:
Preface. i-vii - Yu He, Cristian Bodnar, Pietro Liò:
Sheaf-based Positional Encodings for Graph Neural Networks. 1-18 - Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Liò:
AMES: A differentiable embedding space selection framework for latent graph inference. 19-34 - Duc Thien Nguyen, Manh Duc Tuan Nguyen, Truong Son Hy, Risi Kondor:
Fast Temporal Wavelet Graph Neural Networks. 35-54 - Cody Tipton, Elizabeth Coda, Davis Brown, Alyson Bittner, Jung Lee, Grayson Jorgenson, Tegan Emerson, Henry Kvinge:
Haldane bundles: a dataset for learning to predict the Chern number of line bundles on the torus. 55-74 - Henry Kvinge, Grayson Jorgenson, Davis Brown, Charles Godfrey, Tegan Emerson:
Internal representations of vision models through the lens of frames on data manifolds. 75-115 - Moein Khajehnejad, Forough Habibollahi, Alon Loeffler, Brett J. Kagan, Adeel Razi:
On complex network dynamics of an in vitro neuronal system during Rest and Gameplay. 116-128 - Sho Sonoda, Hideyuki Ishi, Isao Ishikawa, Masahiro Ikeda:
Joint Group Invariant Functions on Data-Parameter Domain Induce Universal Neural Networks. 129-144 - Chuqin Geng, Xiaojie Xu, Haolin Ye, Xujie Si:
Scalar Invariant Networks with Zero Bias. 145-163 - Rubén Ballester, Carles Casacuberta, Sergio Escalera:
Decorrelating neurons using persistence. 164-182 - Marco Pegoraro, Riccardo Marin, Arianna Rampini, Simone Melzi, Luca Cosmo, Emanuele Rodolà:
Spectral Maps for Learning on Subgraphs. 183-205 - Antonio Briola, Yuanrong Wang, Silvia Bartolucci, Tomaso Aste:
Homological Convolutional Neural Networks. 206-231 - Xinran Han, Todd E. Zickler:
Curvature Fields from Shading Fields. 232-254 - Christopher Versteeg, Andrew R. Sedler, Jonathan D. McCart, Chethan Pandarinath:
Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity. 255-278 - Suryaka Suresh, Vinayak Abrol, Anshul Thakur:
Pitfalls in Measuring Neural Transferability. 279-291 - Philip Andrew Mansfield, Arash Afkanpour, Warren Richard Morningstar, Karan Singhal:
Random field augmentations for self-supervised representation learning. 292-302 - Colin Kohler, Nathan Vaska, Ramya Muthukrishnan, Whangbong Choi, Jung Yeon Park, Justin Goodwin, Rajmonda Caceres, Robin Walters:
Symmetric models for radar response modeling. 303-323 - Thomas Walker, Octave Mariotti, Amir Vaxman, Hakan Bilen:
Explicit neural surfaces: learning continuous geometry with deformation fields. 324-345 - Jiaang Li, Antonia Karamolegkou, Yova Kementchedjhieva, Mostafa Abdou, Anders Søgaard:
Structural Similarities Between Language Models and Neural Response Measurements. 346-365 - Aditya Chetan, Nipun Kwatra:
Distance Learner: Incorporating Manifold Prior to Model Training. 366-387 - Donato Crisostomi, Irene Cannistraci, Luca Moschella, Pietro Barbiero, Marco Ciccone, Pietro Liò, Emanuele Rodolà:
From charts to atlas: Merging latent spaces into one. 388-404 - James Mochizuki-Freeman, Md Rysul Kabir, Mitesh Gulecha, Zoran Tiganj:
Geometry of abstract learned knowledge in deep RL agents. 405-424 - John J. Vastola:
Optimal packing of attractor states in neural representations. 425-442 - Arif Dönmez:
Discovering latent causes and memory modification: A computational approach using symmetry and geometry. 443-458
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