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ORCIDAndrew M. Saxe
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2020 – today
- 2024
[c28]Jin Hwa Lee, Stefano Sarao Mannelli, Andrew M. Saxe:
Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning. ICML 2024- [c27]Stefano Sarao Mannelli, Yaraslau Ivashinka, Andrew M. Saxe, Luca Saglietti:
Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks. ICML 2024 - [c26]Loek van Rossem, Andrew M. Saxe:
When Representations Align: Universality in Representation Learning Dynamics. ICML 2024 - [c25]Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe:
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation. ICML 2024 - [c24]Yedi Zhang, Peter E. Latham, Andrew M. Saxe:
Understanding Unimodal Bias in Multimodal Deep Linear Networks. ICML 2024 - [i31]Loek van Rossem, Andrew M. Saxe:
When Representations Align: Universality in Representation Learning Dynamics. CoRR abs/2402.09142 (2024) - [i30]Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe:
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation. CoRR abs/2404.07129 (2024) - [i29]Stefano Sarao Mannelli, Yaraslau Ivashinka, Andrew M. Saxe, Luca Saglietti:
Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks. CoRR abs/2406.01589 (2024) - [i28]Daniel Kunin, Allan Raventós, Clémentine Dominé, Feng Chen, David Klindt, Andrew M. Saxe, Surya Ganguli:
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning. CoRR abs/2406.06158 (2024) - [i27]Yedi Zhang, Andrew M. Saxe, Peter E. Latham:
When Are Bias-Free ReLU Networks Like Linear Networks? CoRR abs/2406.12615 (2024) - [i26]Jirko Rubruck, Jan P. Bauer, Andrew M. Saxe, Christopher Summerfield:
Early learning of the optimal constant solution in neural networks and humans. CoRR abs/2406.17467 (2024) - [i25]Clémentine C. J. Dominé, Nicolas Anguita, Alexandra M. Proca, Lukas Braun, Daniel Kunin, Pedro A. M. Mediano, Andrew M. Saxe:
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks. CoRR abs/2409.14623 (2024) - [i24]Devon Jarvis, Richard Klein, Benjamin Rosman, Andrew M. Saxe:
On The Specialization of Neural Modules. CoRR abs/2409.14981 (2024) - 2023
- [j4]Timo Flesch
, David G. Nagy, Andrew M. Saxe, Christopher Summerfield:
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals. PLoS Comput. Biol. 19(1) (2023) - [c23]Aaditya K. Singh, David Ding, Andrew M. Saxe, Felix Hill, Andrew K. Lampinen:
Know your audience: specializing grounded language models with listener subtraction. EACL 2023: 3866-3893 - [c22]Devon Jarvis, Richard Klein, Benjamin Rosman, Andrew M. Saxe:
On The Specialization of Neural Modules. ICLR 2023 - [c21]Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill:
The Transient Nature of Emergent In-Context Learning in Transformers. NeurIPS 2023 - [i23]Anika T. Löwe, Léo Touzo, Paul S. Muhle-Karbe, Andrew M. Saxe, Christopher Summerfield, Nicolas W. Schuck:
Regularised neural networks mimic human insight. CoRR abs/2302.11351 (2023) - [i22]Nishil Patel, Sebastian Lee, Stefano Sarao Mannelli, Sebastian Goldt, Andrew M. Saxe:
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions. CoRR abs/2306.10404 (2023) - [i21]Rodrigo Carrasco-Davis, Javier Masís, Andrew M. Saxe:
Meta-Learning Strategies through Value Maximization in Neural Networks. CoRR abs/2310.19919 (2023) - [i20]Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill:
The Transient Nature of Emergent In-Context Learning in Transformers. CoRR abs/2311.08360 (2023) - [i19]Yedi Zhang, Peter E. Latham, Andrew M. Saxe:
A Theory of Unimodal Bias in Multimodal Learning. CoRR abs/2312.00935 (2023) - 2022
- [j3]Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe, Lenka Zdeborová:
Probing transfer learning with a model of synthetic correlated datasets. Mach. Learn. Sci. Technol. 3(1): 15030 (2022) - [c20]Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew M. Saxe:
Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation. ICML 2022: 12455-12477 - [c19]Andrew M. Saxe, Shagun Sodhani, Sam Jay Lewallen:
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks. ICML 2022: 19287-19309 - [c18]Lukas Braun, Clémentine Dominé, James Fitzgerald, Andrew M. Saxe:
Exact learning dynamics of deep linear networks with prior knowledge. NeurIPS 2022 - [c17]Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe:
An Analytical Theory of Curriculum Learning in Teacher-Student Networks. NeurIPS 2022 - [i18]Timo Flesch, David G. Nagy, Andrew M. Saxe, Christopher Summerfield:
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals. CoRR abs/2203.11560 (2022) - [i17]Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew M. Saxe:
Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation. CoRR abs/2205.09029 (2022) - [i16]Aaditya K. Singh, David Ding, Andrew M. Saxe, Felix Hill, Andrew K. Lampinen:
Know your audience: specializing grounded language models with the game of Dixit. CoRR abs/2206.08349 (2022) - [i15]Andrew M. Saxe, Shagun Sodhani, Sam Jay Lewallen:
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks. CoRR abs/2207.10430 (2022) - [i14]Timo Flesch, Andrew M. Saxe, Christopher Summerfield:
Continual task learning in natural and artificial agents. CoRR abs/2210.04520 (2022) - 2021
- [c16]Sebastian Lee, Sebastian Goldt, Andrew M. Saxe:
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity. ICML 2021: 6109-6119 - [i13]Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe, Lenka Zdeborová:
Probing transfer learning with a model of synthetic correlated datasets. CoRR abs/2106.05418 (2021) - [i12]Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe:
An Analytical Theory of Curriculum Learning in Teacher-Student Networks. CoRR abs/2106.08068 (2021) - [i11]Sebastian Lee, Sebastian Goldt, Andrew M. Saxe:
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity. CoRR abs/2107.04384 (2021) - 2020
- [j2]Madhu S. Advani, Andrew M. Saxe, Haim Sompolinsky:
High-dimensional dynamics of generalization error in neural networks. Neural Networks 132: 428-446 (2020) - [c15]Yinan Cao, Christopher Summerfield, Andrew M. Saxe:
Characterizing emergent representations in a space of candidate learning rules for deep networks. NeurIPS 2020
2010 – 2019
- 2019
- [c14]Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová:
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. NeurIPS 2019: 6979-6989 - [i10]Sebastian Goldt, Madhu S. Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová:
Generalisation dynamics of online learning in over-parameterised neural networks. CoRR abs/1901.09085 (2019) - [i9]Sebastian Goldt, Madhu S. Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová:
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. CoRR abs/1906.08632 (2019) - 2018
- [c13]Adam Christopher Earle, Andrew M. Saxe, Benjamin Rosman:
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization. ICLR (Poster) 2018 - [c12]Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox:
On the Information Bottleneck Theory of Deep Learning. ICLR (Poster) 2018 - [i8]Yao Zhang, Andrew M. Saxe, Madhu S. Advani, Alpha A. Lee:
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning. CoRR abs/1803.01927 (2018) - [i7]Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe:
Minnorm training: an algorithm for training over-parameterized deep neural networks. CoRR abs/1806.00730 (2018) - [i6]Andrew M. Saxe, James L. McClelland, Surya Ganguli:
A mathematical theory of semantic development in deep neural networks. CoRR abs/1810.10531 (2018) - 2017
- [c11]Andrew M. Saxe, Adam Christopher Earle, Benjamin Rosman:
Hierarchy Through Composition with Multitask LMDPs. ICML 2017: 3017-3026 - [i5]Adam Christopher Earle, Andrew M. Saxe, Benjamin Rosman:
Hierarchical Subtask Discovery With Non-Negative Matrix Factorization. CoRR abs/1708.00463 (2017) - [i4]Madhu S. Advani, Andrew M. Saxe:
High-dimensional dynamics of generalization error in neural networks. CoRR abs/1710.03667 (2017) - 2016
- [c10]James L. McClelland, Steven Stenberg Hansen, Andrew M. Saxe:
Tutorial Workshop on Contemporary Deep Neural Network Models. CogSci 2016 - [c9]Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox:
Tensor Switching Networks. NIPS 2016: 2038-2046 - [i3]Tommaso Furlanello, Jiaping Zhao, Andrew M. Saxe, Laurent Itti, Bosco S. Tjan:
Active Long Term Memory Networks. CoRR abs/1606.02355 (2016) - [i2]Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox:
Tensor Switching Networks. CoRR abs/1610.10087 (2016) - [i1]Andrew M. Saxe, Adam Christopher Earle, Benjamin Rosman:
Hierarchy through Composition with Linearly Solvable Markov Decision Processes. CoRR abs/1612.02757 (2016) - 2014
- [c8]Rachel Lee, Andrew M. Saxe:
Modeling Perceptual Learning with Deep Networks. CogSci 2014 - [c7]Andrew M. Saxe:
Deep Learning and the Brain. CogSci 2014 - [c6]Andrew M. Saxe:
Multitask model-free reinforcement learning. CogSci 2014 - [c5]Andrew M. Saxe, James L. McClelland, Surya Ganguli:
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. ICLR 2014 - 2013
- [c4]Andrew M. Saxe, James L. McClelland, Surya Ganguli:
Learning hierarchical categories in deep neural networks. CogSci 2013 - 2011
- [c3]Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng:
On Random Weights and Unsupervised Feature Learning. ICML 2011: 1089-1096 - [c2]Andrew M. Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng:
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity. NIPS 2011: 1971-1979
2000 – 2009
- 2009
- [c1]Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng:
Measuring Invariances in Deep Networks. NIPS 2009: 646-654 - 2006
- [j1]Anand R. Atreya, Bryan C. Cattle, Brendan M. Collins, Benjamin Essenburg, Gordon H. Franken, Andrew M. Saxe, Scott N. Schiffres, Alain L. Kornhauser:
Prospect Eleven: Princeton University's entry in the 2005 DARPA Grand Challenge. J. Field Robotics 23(9): 745-753 (2006)
Coauthor Index
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last updated on 2024-10-16 20:34 CEST by the dblp team
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