Andrew M. Saxe

According to our database, Andrew M. Saxe authored at least 50 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks.

Anika T. Löwe, Léo Touzo, Paul S. Muhle-Karbe, Andrew M. Saxe, Christopher Summerfield, Nicolas W. Schuck

PLoS Comput. Biol., 2024

From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks.

Clémentine C. J. Dominé, Nicolas Anguita, Alexandra M. Proca, Lukas Braun, Daniel Kunin, Pedro A. M. Mediano, Andrew M. Saxe

CoRR, 2024

Early learning of the optimal constant solution in neural networks and humans.

Jirko Rubruck, Jan P. Bauer, Andrew M. Saxe, Christopher Summerfield

CoRR, 2024

When Are Bias-Free ReLU Networks Like Linear Networks?

Yedi Zhang, Andrew M. Saxe, Peter E. Latham

CoRR, 2024

Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning.

Daniel Kunin, Allan Raventós, Clémentine Dominé, Feng Chen, David A. Klindt, Andrew M. Saxe, Surya Ganguli

CoRR, 2024

Understanding Unimodal Bias in Multimodal Deep Linear Networks.

Yedi Zhang, Peter E. Latham, Andrew M. Saxe

Proceedings of the Forty-first International Conference on Machine Learning, 2024

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation.

Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe

Proceedings of the Forty-first International Conference on Machine Learning, 2024

When Representations Align: Universality in Representation Learning Dynamics.

Loek van Rossem, Andrew M. Saxe

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks.

Stefano Sarao Mannelli, Yaraslau Ivashinka, Andrew M. Saxe, Luca Saglietti

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning.

Jin Hwa Lee, Stefano Sarao Mannelli, Andrew M. Saxe

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.

Timo Flesch, David G. Nagy, Andrew M. Saxe, Christopher Summerfield

PLoS Comput. Biol., January, 2023

A Theory of Unimodal Bias in Multimodal Learning.

Yedi Zhang, Peter E. Latham, Andrew M. Saxe

CoRR, 2023

Meta-Learning Strategies through Value Maximization in Neural Networks.

Rodrigo Carrasco-Davis, Javier Masís, Andrew M. Saxe

CoRR, 2023

The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions.

Nishil Patel, Sebastian Lee, Stefano Sarao Mannelli, Sebastian Goldt, Andrew M. Saxe

CoRR, 2023

Regularised neural networks mimic human insight.

Anika T. Löwe, Léo Touzo, Paul S. Muhle-Karbe, Andrew M. Saxe, Christopher Summerfield, Nicolas W. Schuck

CoRR, 2023

The Transient Nature of Emergent In-Context Learning in Transformers.

Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On The Specialization of Neural Modules.

Devon Jarvis, Richard Klein, Benjamin Rosman, Andrew M. Saxe

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Know your audience: specializing grounded language models with listener subtraction.

Aaditya K. Singh, David Ding, Andrew M. Saxe, Felix Hill, Andrew K. Lampinen

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

2022

Probing transfer learning with a model of synthetic correlated datasets.

Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe, Lenka Zdeborová

Mach. Learn. Sci. Technol., 2022

Continual task learning in natural and artificial agents.

Timo Flesch, Andrew M. Saxe, Christopher Summerfield

CoRR, 2022

Know your audience: specializing grounded language models with the game of Dixit.

Aaditya K. Singh, David Ding, Andrew M. Saxe, Felix Hill, Andrew K. Lampinen

CoRR, 2022

An Analytical Theory of Curriculum Learning in Teacher-Student Networks.

Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Exact learning dynamics of deep linear networks with prior knowledge.

Lukas Braun, Clémentine Dominé, James Fitzgerald, Andrew M. Saxe

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Neural Race Reduction: Dynamics of Abstraction in Gated Networks.

Andrew M. Saxe, Shagun Sodhani, Sam Jay Lewallen

Proceedings of the International Conference on Machine Learning, 2022

Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation.

Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew M. Saxe

Proceedings of the International Conference on Machine Learning, 2022

2021

Continual Learning in the Teacher-Student Setup: Impact of Task Similarity.

Sebastian Lee, Sebastian Goldt, Andrew M. Saxe

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

High-dimensional dynamics of generalization error in neural networks.

Madhu S. Advani, Andrew M. Saxe, Haim Sompolinsky

Neural Networks, 2020

Characterizing emergent representations in a space of candidate learning rules for deep networks.

Yinan Cao, Christopher Summerfield, Andrew M. Saxe

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Generalisation dynamics of online learning in over-parameterised neural networks.

Sebastian Goldt, Madhu S. Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová

CoRR, 2019

Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.

Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

A mathematical theory of semantic development in deep neural networks.

Andrew M. Saxe, James L. McClelland, Surya Ganguli

CoRR, 2018

Minnorm training: an algorithm for training over-parameterized deep neural networks.

Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe

CoRR, 2018

Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning.

Yao Zhang, Andrew M. Saxe, Madhu S. Advani, Alpha A. Lee

CoRR, 2018

On the Information Bottleneck Theory of Deep Learning.

Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox

Proceedings of the 6th International Conference on Learning Representations, 2018

Hierarchical Subtask Discovery with Non-Negative Matrix Factorization.

Adam Christopher Earle, Andrew M. Saxe, Benjamin Rosman

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

High-dimensional dynamics of generalization error in neural networks.

Madhu S. Advani, Andrew M. Saxe

CoRR, 2017

Hierarchy Through Composition with Multitask LMDPs.

Andrew M. Saxe, Adam Christopher Earle, Benjamin Rosman

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Hierarchy through Composition with Linearly Solvable Markov Decision Processes.

Andrew M. Saxe, Adam Christopher Earle, Benjamin Rosman

CoRR, 2016

Active Long Term Memory Networks.

Tommaso Furlanello, Jiaping Zhao, Andrew M. Saxe, Laurent Itti, Bosco S. Tjan

CoRR, 2016

Tensor Switching Networks.

Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Tutorial Workshop on Contemporary Deep Neural Network Models.

James L. McClelland, Steven Stenberg Hansen, Andrew M. Saxe

Proceedings of the 38th Annual Meeting of the Cognitive Science Society, 2016

2014

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

Andrew M. Saxe, James L. McClelland, Surya Ganguli

Proceedings of the 2nd International Conference on Learning Representations, 2014

Multitask model-free reinforcement learning.

Andrew M. Saxe

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

Deep Learning and the Brain.

Andrew M. Saxe

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

Modeling Perceptual Learning with Deep Networks.

Rachel Lee, Andrew M. Saxe

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

2013

Learning hierarchical categories in deep neural networks.

Andrew M. Saxe, James L. McClelland, Surya Ganguli

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

2011

Unsupervised learning models of primary cortical receptive fields and receptive field plasticity.

Andrew M. Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

On Random Weights and Unsupervised Feature Learning.

Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng

Proceedings of the 28th International Conference on Machine Learning, 2011

2009

Measuring Invariances in Deep Networks.

Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2006

Prospect Eleven: Princeton University's entry in the 2005 DARPA Grand Challenge.

Anand R. Atreya, Bryan C. Cattle, Brendan M. Collins, Benjamin Essenburg, Gordon H. Franken, Andrew M. Saxe, Scott N. Schiffres, Alain L. Kornhauser

J. Field Robotics, 2006