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Sophia Sanborn
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2020 – today
- 2024
- [c11]Giovanni Luca Marchetti, Christopher J. Hillar, Danica Kragic, Sophia Sanborn:
Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks. COLT 2024: 3775-3797 - [i12]Simon Mataigne, Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane:
The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks. CoRR abs/2407.07655 (2024) - [i11]Sophia Sanborn, Johan Mathe, Mathilde Papillon, Domas Buracas, Hansen Lillemark, Christian Shewmake, Abby Bertics, Xavier Pennec, Nina Miolane:
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures. CoRR abs/2407.09468 (2024) - 2023
- [c10]Francisco Acosta, Sophia Sanborn
, Khanh Dao Duc, Manu S. Madhav, Nina Miolane:
Quantifying Extrinsic Curvature in Neural Manifolds. CVPR Workshops 2023: 610-619 - [c9]Sophia Sanborn, Christian Shewmake, Bruno A. Olshausen, Christopher J. Hillar:
Bispectral Neural Networks. ICLR 2023 - [c8]Sophia Sanborn, Nina Miolane:
A General Framework for Robust G-Invariance in G-Equivariant Networks. NeurIPS 2023 - [c7]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Preface. TAG-ML 2023: 1-2 - [c6]Mathilde Papillon, Mustafa Hajij, Audun Myers, Florian Frantzen, Ghada Zamzmi, Helen Jenne, Johan Mathe, Josef Hoppe, Michael T. Schaub, Theodore Papamarkou
ICML 2023 Topological Deep Learning Challenge: Design and Results. TAG-ML 2023: 3-8 - [e2]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Topological, Algebraic and Geometric Learning Workshops 2023, 28 July 2023, Honolulu, HI, USA. Proceedings of Machine Learning Research 221, PMLR 2023 [contents] - [i10]Mathilde Papillon, Sophia Sanborn
Architectures of Topological Deep Learning: A Survey on Topological Neural Networks. CoRR abs/2304.10031 (2023) - [i9]Mathilde Papillon, Mustafa Hajij, Florian Frantzen, Josef Hoppe, Helen Jenne, Johan Mathe, Audun Myers, Theodore Papamarkou, Michael T. Schaub, Ghada Zamzmi, Tolga Birdal, Tamal K. Dey, Tim Doster, Tegan Emerson, Gurusankar Gopalakrishnan, Devendra Govil, Vincent P. Grande, Aldo Guzmán-Sáenz, Henry Kvinge, Neal Livesay, Jan Meissner, Soham Mukherjee, Shreyas N. Samaga, Karthikeyan Natesan Ramamurthy, Maneel Reddy Karri, Paul Rosen, Sophia Sanborn
ICML 2023 Topological Deep Learning Challenge : Design and Results. CoRR abs/2309.15188 (2023) - [i8]David A. Klindt, Sophia Sanborn
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems. CoRR abs/2310.11431 (2023) - [i7]Sophia Sanborn
A General Framework for Robust G-Invariance in G-Equivariant Networks. CoRR abs/2310.18564 (2023) - [i6]Carlos G. Correa, Sophia Sanborn, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw
Exploring the hierarchical structure of human plans via program generation. CoRR abs/2311.18644 (2023) - [i5]Giovanni Luca Marchetti, Christopher Hillar, Danica Kragic, Sophia Sanborn:
Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks. CoRR abs/2312.08550 (2023) - 2022
- [j2]Edward Paxon Frady
Efficient Neuromorphic Signal Processing with Resonator Neurons. J. Signal Process. Syst. 94(10): 917-927 (2022) - [c5]Sophia Sanborn, Christian Shewmake, Simone Azeglio, Arianna Di Bernardo, Nina Miolane:
Preface. NeurReps 2022: i-vi - [c4]Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, Claire Donnat, Johan Mathe, Rishi Sonthalia, Xinyue Cui, Tom Szwagier, Arthur Pignet, Andri Bergsson, Søren Hauberg, Dmitriy Nielsen, Stefan Sommer, David A. Klindt, Erik Hermansen, Melvin Vaupel, Benjamin A. Dunn, Jeffrey Xiong, Noga Aharony, Itsik Pe'er, Felix Ambellan, Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz, Nina Miolane:
ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results. TAG-ML 2022: 269-276 - [e1]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 [contents] - [i4]Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results. CoRR abs/2206.09048 (2022) - [i3]Sophia Sanborn, Christian Shewmake, Bruno A. Olshausen, Christopher Hillar:
Bispectral Neural Networks. CoRR abs/2209.03416 (2022) - 2021
- [c3]Garrick Orchard, Edward Paxon Frady, Daniel Ben Dayan Rubin, Sophia Sanborn
Efficient Neuromorphic Signal Processing with Loihi 2. SiPS 2021: 254-259 - [i2]Garrick Orchard, Edward Paxon Frady, Daniel Ben Dayan Rubin, Sophia Sanborn, Sumit Bam Shrestha, Friedrich T. Sommer, Mike Davies:
Efficient Neuromorphic Signal Processing with Loihi 2. CoRR abs/2111.03746 (2021)
2010 – 2019
- 2019
- [j1]Joseph L. Austerweil, Sophia Sanborn
Learning How to Generalize. Cogn. Sci. 43(8) (2019) - 2018
- [c2]Sophia Sanborn, David Bourgin, Michael Chang, Tom Griffiths:
Representational efficiency outweighs action efficiency in human program induction. CogSci 2018 - [i1]Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths:
Representational efficiency outweighs action efficiency in human program induction. CoRR abs/1807.07134 (2018) - 2016
- [c1]Rebecca Zhu, Jean-Rémy Hochmann, Sophia Sanborn, Susan Carey:
Representations of Entropy and of the Relations Same and Different Early in Human Development. CogSci 2016
Coauthor Index
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last updated on 2024-10-07 21:25 CEST by the dblp team
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