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Number of items: 10.
D
Domine, Clementine;
Anguita, Nicolas;
Proca, Alexandra;
Braun, Lukas;
Mediano, Pedro;
Saxe, Andrew;
(2025)
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks.
In:
Proceedings of the ICLR 2025 Conference.
(pp. pp. 1-52).
ICLR
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Dorrell, william;
Hsu, Kyle;
Hollingsworth, Luke;
Lee, Jin Hwa;
Wu, Jiajun;
Finn, Chelsea;
Latham, Peter;
... Whittington, James CR; + view all
(2025)
Range, not Independence, Drives Modularity in Biologically Inspired Representations.
In:
(Proceedings) The Thirteenth International Conference on Learning Representations.
(In press).
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Galashov, alexandre;
De Bortoli, Valentin;
Gretton, Arthur;
(2025)
Deep MMD Gradient Flow without adversarial training.
In:
(Proceedings) The Thirteenth International Conference on Learning Representations.
(In press).
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J
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Jarvis, Devon;
Klein, Richard;
Rosman, Benjamin;
Saxe, Andrew M;
(2025)
Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks.
In:
Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025).
(pp. pp. 1-35).
OpenReview.net: Singapore, Singapore.
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Jarvis, Devon;
Lee, Sebastian;
Domine, Clementine;
Saxe, andrew;
Sarao Mannelli, Stefano;
(2025)
A Theory of Initialisation's Impact on Specialisation.
In:
Proceedings of the ICLR 2025 Conference.
(pp. pp. 1-29).
ICLR
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K
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Kim, Juno;
Meunier, Dimitri;
Gretton, Arthur;
Suzuki, Taiji;
Li, Zhu;
(2025)
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression.
In:
(Proceedings) The Thirteenth International Conference on Learning Representations.
(In press).
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M
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Mirramezani, Mehran;
Meeussen, Anne;
Bertoldi, Katia;
Orbanz, Peter;
Adam, Ryan P;
(2025)
Designing Mechanical Meta-Materials by Learning Equivariant Flows.
In:
(Proceedings) The Thirteenth International Conference on Learning Representations.
(In press).
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S
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Sclocchi, Antonio;
Favero, Alessandro;
Wyart, Matthieu;
(2025)
A phase transition in diffusion models reveals the hierarchical nature of data.
Proceedings of the National Academy of Sciences (PNAS)
, 122
(1)
, Article e2408799121. 10.1073/pnas.2408799121.
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Y
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Yu, Changmin;
Sahani, Maneesh;
Lengyel, Máté;
(2025)
Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA.
In:
(Proceedings) The Thirteenth International Conference on Learning Representations.
(In press).
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Z
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Zhang, Yedi;
Saxe, Andrew;
Latham, peter;
(2025)
When Are Bias-Free ReLU Networks Effectively Linear Networks?
Transactions on Machine Learning Research
, 04
pp. 1-36.
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