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)

Measuring Invariances in Deep Networks. - Ian J. Goodfellow; Quoc V. Le; Andrew M. Saxe; Honglak Lee; Andrew Y. Ng (NIPS, 2009)

On Random Weights and Unsupervised Feature Learning. - Andrew M. Saxe; Pang Wei Koh; Zhenghao Chen; Maneesh Bhand; Bipin Suresh; Andrew Y. Ng (ICML, 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 (NIPS, 2011)

Learning hierarchical categories in deep neural networks. - Andrew M. Saxe; James L. McClelland; Surya Ganguli (CogSci, 2013)

Modeling Perceptual Learning with Deep Networks. - Rachel Lee; Andrew M. Saxe (CogSci, 2014)

Deep Learning and the Brain. - Andrew M. Saxe (CogSci, 2014)

Multitask model-free reinforcement learning. - Andrew M. Saxe (CogSci, 2014)

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. - Andrew M. Saxe; James L. McClelland; Surya Ganguli (ICLR, 2014)

Tutorial Workshop on Contemporary Deep Neural Network Models. - James L. McClelland; Steven Stenberg Hansen; Andrew M. Saxe (CogSci, 2016)

Tensor Switching Networks. - Chuan-Yung Tsai; Andrew M. Saxe; David D. Cox (NIPS, 2016)

Active Long Term Memory Networks. - Tommaso Furlanello; Jiaping Zhao; Andrew M. Saxe; Laurent Itti; Bosco S. Tjan (CoRR, 2016)

Hierarchy through Composition with Linearly Solvable Markov Decision Processes. - Andrew M. Saxe; Adam Christopher Earle; Benjamin Rosman (CoRR, 2016)

Hierarchy Through Composition with Multitask LMDPs. - Andrew M. Saxe; Adam Christopher Earle; Benjamin Rosman (ICML, 2017)

High-dimensional dynamics of generalization error in neural networks. - Madhu S. Advani; Andrew M. Saxe (CoRR, 2017)

Hierarchical Subtask Discovery with Non-Negative Matrix Factorization. - Adam Christopher Earle; Andrew M. Saxe; Benjamin Rosman (ICLR (Poster), 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 (ICLR (Poster), 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)

Minnorm training: an algorithm for training over-parameterized deep neural networks. - Yamini Bansal; Madhu Advani; David D. Cox; Andrew M. Saxe (CoRR, 2018)

A mathematical theory of semantic development in deep neural networks. - Andrew M. Saxe; James L. McClelland; Surya Ganguli (CoRR, 2018)

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 (NeurIPS, 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)

Characterizing emergent representations in a space of candidate learning rules for deep networks. - Yinan Cao; Christopher Summerfield; Andrew M. Saxe (NeurIPS, 2020)

High-dimensional dynamics of generalization error in neural networks. - Madhu S. Advani; Andrew M. Saxe; Haim Sompolinsky (Neural Networks, 2020)

Continual Learning in the Teacher-Student Setup: Impact of Task Similarity. - Sebastian Lee; Sebastian Goldt; Andrew M. Saxe (ICML, 2021)

Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation. - Sebastian Lee; Stefano Sarao Mannelli; Claudia Clopath; Sebastian Goldt; Andrew M. Saxe (ICML, 2022)

The Neural Race Reduction: Dynamics of Abstraction in Gated Networks. - Andrew M. Saxe; Shagun Sodhani; Sam Jay Lewallen (ICML, 2022)

Exact learning dynamics of deep linear networks with prior knowledge. - Lukas Braun; Clémentine C. J. Dominé; James Fitzgerald; Andrew M. Saxe (NeurIPS, 2022)

An Analytical Theory of Curriculum Learning in Teacher-Student Networks. - Luca Saglietti; Stefano Sarao Mannelli; Andrew M. Saxe (NeurIPS, 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)

Continual task learning in natural and artificial agents. - Timo Flesch; Andrew M. Saxe; Christopher Summerfield (CoRR, 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)

Know your audience: specializing grounded language models with listener subtraction. - Aaditya K. Singh; David Ding; Andrew M. Saxe; Felix Hill; Andrew K. Lampinen (EACL, 2023)

On The Specialization of Neural Modules. - Devon Jarvis; Richard Klein; Benjamin Rosman; Andrew M. Saxe (ICLR, 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 (NeurIPS, 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 RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions. - Nishil Patel; Sebastian Lee; Stefano Sarao Mannelli; Sebastian Goldt; Andrew M. Saxe (CoRR, 2023)

Meta-Learning Strategies through Value Maximization in Neural Networks. - Rodrigo Carrasco-Davis; Javier Masís; Andrew M. Saxe (CoRR, 2023)

A Theory of Unimodal Bias in Multimodal Learning. - Yedi Zhang; Peter E. Latham; Andrew M. Saxe (CoRR, 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., 2023)

Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning. - Jin Hwa Lee; Stefano Sarao Mannelli; Andrew M. Saxe (ICML, 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 (ICML, 2024)

When Representations Align: Universality in Representation Learning Dynamics. - Loek van Rossem; Andrew M. Saxe (ICML, 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 (ICML, 2024)

Understanding Unimodal Bias in Multimodal Deep Linear Networks. - Yedi Zhang; Peter E. Latham; Andrew M. Saxe (ICML, 2024)

Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning. - Daniel Kunin; Allan Raventós; Clémentine C. J. Dominé; Feng Chen 046; David A. Klindt; Andrew M. Saxe; Surya Ganguli (NeurIPS, 2024)

Nonlinear dynamics of localization in neural receptive fields. - Leon Lufkin; Andrew M. Saxe; Erin Grant (NeurIPS, 2024)

Flexible task abstractions emerge in linear networks with fast and bounded units. - Kai Sandbrink; Jan P. Bauer; Alexandra M. Proca; Andrew M. Saxe; Christopher Summerfield; Ali Hummos (NeurIPS, 2024)

When Are Bias-Free ReLU Networks Like Linear Networks? - Yedi Zhang; Andrew M. Saxe; Peter E. Latham (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)

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 Carla Juliette Dominé; Nicolas Anguita; Alexandra M. Proca; Lukas Braun; Daniel Kunin; Pedro A. M. Mediano; Andrew M. Saxe (ICLR, 2025)

Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks. - Devon Jarvis; Richard Klein; Benjamin Rosman; Andrew M. Saxe (ICLR, 2025)

A Theory of Initialisation's Impact on Specialisation. - Devon Jarvis; Sebastian Lee; Clémentine Carla Juliette Dominé; Andrew M. Saxe; Stefano Sarao Mannelli (ICLR, 2025)

When Are Bias-Free ReLU Networks Effectively Linear Networks? - Yedi Zhang; Andrew M. Saxe; Peter E. Latham (Trans. Mach. Learn. Res., 2025)

Training Dynamics of In-Context Learning in Linear Attention. - Yedi Zhang; Aaditya K. Singh; Peter E. Latham; Andrew M. Saxe (CoRR, 2025)

Revisiting the Role of Relearning in Semantic Dementia. - Devon Jarvis; Verena Klar; Richard Klein; Benjamin Rosman; Andrew M. Saxe (CoRR, 2025)

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning. - Aaditya K. Singh; Ted Moskovitz; Sara Dragutinovic; Felix Hill; Stephanie C. Y. Chan; Andrew M. Saxe (CoRR, 2025)

Distinct Computations Emerge From Compositional Curricula in In-Context Learning. - Jin Hwa Lee; Andrew K. Lampinen; Aaditya K. Singh; Andrew M. Saxe (CoRR, 2025)

Algorithm Development in Neural Networks: Insights from the Streaming Parity Task. - Loek van Rossem; Andrew M. Saxe (CoRR, 2025)