Brenden M. Lake

Assistant Professor of Psychology and Data Science
Department of Psychology and Center for Data Science (joint)
Department of Computer Science and Center for Neural Science (affiliate)
NYU Minds, Brains, and Machines Initiative (Co-director)
Human & Machine Learning Lab and CILVR Lab (research groups)
New York University

Research Scientist
Facebook AI Research


Contact

Email: brenden@nyu.edu
Phone: (212) 998-3059
Data Science office: 60 5th Ave., Room 610, New York, NY 10011
Psychology office: 6 Washington Place, Room 858, New York, NY 10003
Curriculum Vitae
Lab website

Interested in joining the lab? Check here for info about open positions.
Please also see this page about Computational Cognitive Science at NYU.


Research

Our lab aims to understand the ingredients of intelligence. We use advances in machine intelligence to better understand human intelligence, and use insights from human intelligence to develop more fruitful kinds of machine intelligence.

In our pursuits, we study human cognitive abilities that elude the best AI systems. While there are many to choose from, our current focus includes concept learning, compositional generalization, question asking, goal generation, and abstract reasoning. Our technical focus includes neuro-symbolic modeling and learning “through the eyes of a child” on developmentally-realistic datasets.

By studying distinctively human endeavors, there is opportunity to advance both cognitive science and AI. In cognitive science, if people have abilities beyond the reach of algorithms, then we do not fully understand how these abilities work. In AI, these abilities are important open problems with opportunities to reverse-engineer the human solutions.


Teaching

PSYCH-GA 3405.004 / DS-GS 1016 : Computational cognitive modeling (current; Spring 2022)
PSYCH-GA 3405.001 : Categories and Concepts (last taught Fall 2021)
PSYCH-UA 46 : Lab in Cognition and Perception (last taught Fall 2021)
PSYCH-GA 3405.001 / DS-GA 3001.014 : Advancing AI through cognitive science (last taught Spring 2019)


Publications

See below for publications by year: Preprints, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015 and earlier. Check out our lab page for a potentially more recent list.


2022

Vong, W. K. and Lake, B. M. (in press). Cross-situational word learning with multimodal neural networks. Cognitive Science.


2021

Gandhi, K., Stojnic, G., Lake, B. M. and Dillon, M. R. (2021). Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others. Advances in Neural Information Processing Systems 34 . [Supporting Info.][Dataset]

Nye, M., Tessler, M. H., Tenenbaum, J. B., and Lake, B. M. (2021). Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning. Advances in Neural Information Processing Systems 34 . [Supporting Info.]

Lake, B. M. and Murphy, G. L. (2021). Word meaning in minds and machines. Psychological Review.

Zhou, Y. and Lake, B. M. (2021). Flexible compositional learning of structured visual concepts. In Proceedings of the 43rd Annual Conference of the Cognitive Science Society.

Tartaglini, A. R., Vong, W. K., and Lake, B. M. (2021). Modeling artificial category learning from pixels: Revisiting Shepard, Hovland, and Jenkins (1961) with deep neural networks. In Proceedings of the 43rd Annual Conference of the Cognitive Science Society.

Davidson, G. and Lake, B. M. (2021). Examining Infant Relation Categorization Through Deep Neural Networks. In Proceedings of the 43rd Annual Conference of the Cognitive Science Society.

Johnson, A., Vong, W. K., Lake, B. M. and Gureckis, T. M. (2021). Fast and flexible: Human program induction in abstract reasoning tasks. In Proceedings of the 43rd Annual Conference of the Cognitive Science Society.

Wang, Z. and Lake, B. M. (2021). Modeling question asking using neural program generation. In Proceedings of the 43rd Annual Conference of the Cognitive Science Society. [Code]

Vedantam, R., Szlam, A., Nickel M., Morcos, A., and Lake, B. M. (2021). CURI: A Benchmark for Productive Concept Learning Under Uncertainty. International Conference on Machine Learning (ICML). [Supporting Info.][Data and Code]

Feinman, R. and Lake, B. M. (2021). Learning Task-General Representations with Generative Neuro-Symbolic Modeling. International Conference on Learning Representations (ICLR). [Code]


2020

Orhan, A. E., Gupta, V. B., and Lake, B. M. (2020). Self-supervised learning through the eyes of a child. Advances in Neural Information Processing Systems 33. [Supporting Info.] [Code and pre-trained models]

Ruis, L., Andreas, J., Baroni, M. Bouchacourt, D., and Lake, B. M. (2020). A Benchmark for Systematic Generalization in Grounded Language Understanding. Advances in Neural Information Processing Systems 33. [Supporting Info.] [Benchmark] [Baseline model]

Nye, M., Solar-Lezama, A., Tenenbaum, J. B., and Lake, B. M. (2020). Learning Compositional Rules via Neural Program Synthesis. Advances in Neural Information Processing Systems 33. [Supporting Info.] [Code]

Gandhi, K. and Lake, B. M. (2020). Mutual exclusivity as a challenge for deep neural networks. Advances in Neural Information Processing Systems 33. [Supporting Info.]

Feinman, R. and Lake, B. M. (2020). Generating new concepts with hybrid neuro-symbolic models. In Proceedings of the 42nd Annual Conference of the Cognitive Science Society. [Short video] [Supporting Info.]

Davidson, G. and Lake, B. M. (2020). Investigating simple object representations in model-free deep reinforcement learning. In Proceedings of the 42nd Annual Conference of the Cognitive Science Society. [Short video]

Lewis, M., Cristiano, V., Lake, B. M., Kwan, T., and Frank, M. C. (2020). The role of developmental change and linguistic experience in the mutual exclusivity effect. Cognition, 198.

Lake, B. M. and Piantadosi, S. T. (2020). People infer recursive visual concepts from just a few examples. Computational Brain & Behavior, 3(1), 54-65. [Supporting Info.] [Experiments]


2019

Lake, B. M. (2019). Compositional generalization through meta sequence-to-sequence learning. Advances in Neural Information Processing Systems 32. [Code]

Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2019). The Omniglot challenge: a 3-year progress report. Current Opinion in Behavioral Sciences, 29, 97-104.

Feinman, R. and Lake, B. M. (2019). Learning a smooth kernel regularizer for convolutional neural networks. In Proceedings of the 41st Annual Conference of the Cognitive Science Society.

Lake, B. M., Linzen, T., and Baroni, M. (2019). Human few-shot learning of compositional instructions. In Proceedings of the 41st Annual Conference of the Cognitive Science Society.

Rothe, A., Lake, B. M., and Gureckis, T. M. (2019). Asking goal-oriented questions and learning from answers. In Proceedings of the 41st Annual Conference of the Cognitive Science Society.


2018

Rothe, A., Lake, B. M., and Gureckis, T. M. (2018). Do people ask good questions? Computational Brain & Behavior, 1(1), 69-89.

Loula, J., Baroni, M., and Lake, B. M. (2018). Rearranging the familiar: Testing compositional generalization in recurrent networks. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP.

Lake, B. M. and Baroni, M. (2018). Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. International Conference on Machine Learning (ICML). [Supporting Info.] [Data set]

Feinman, R. and Lake, B. M. (2018). Learning inductive biases with simple neural networks. In Proceedings of the 40th Annual Conference of the Cognitive Science Society.

Lake, B. M., Lawrence, N. D., and Tenenbaum, J. B. (2018). The emergence of organizing structure in conceptual representation. Cognitive Science, 42(S3), 809-832. [Supporting Info.] [Code]


2017

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, E253.

Rothe, A., Lake, B. M., and Gureckis, T. M. (2017). Question asking as program generation. Advances in Neural Information Processing Systems 30. [Supporting Info.]


2016

Rothe, A., Lake, B. M., and Gureckis, T. M. (2016). Asking and evaluating natural language questions. In Proceedings of the 38th Annual Conference of the Cognitive Science Society.

Cohen, A. and Lake, B. M. (2016). Searching large hypothesis spaces by asking questions. In Proceedings of the 38th Annual Conference of the Cognitive Science Society.


2015

Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338. [Supporting Info.] [visual Turing tests] [Omniglot data set] [Bayesian Program Learning code]

Monfort, M., Lake, B. M., Ziebart, B. D., Lucey, P., and Tenenbaum, J. B. (2015). Softstar: Heuristic-Guided Probabilistic Inference. Advances in Neural Information Processing Systems 28. [Supporting Info.]

Lake, B. M., Zaremba, W., Fergus, R. and Gureckis, T. M. (2015). Deep Neural Networks Predict Category Typicality Ratings for Images. In Proceedings of the 37th Annual Conference of the Cognitive Science Society. [Data]


2014 and earlier

Lake, B. M. (2014). Towards more human-like concept learning in machines: Compositionality, causality, and learning-to-learn. Ph.D. thesis, MIT.

Lake, B. M., Lee, C.-y., Glass, J. R., and Tenenbaum, J. B. (2014). One-shot learning of generative speech concepts. In Proceedings of the 36th Annual Conference of the Cognitive Science Society. [Supporting Info.]

Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2013). One-Shot Learning by Inverting a Compositional Causal Process. Advances in Neural Information Processing Systems 26. [Supporting Info.]

Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2012). Concept learning as motor program induction: A large-scale empirical study. In Proceedings of the 34th Annual Conference of the Cognitive Science Society. [Supporting Info.]

Lake, B. M., Salakhutdinov, R., Gross, J., and Tenenbaum, J. B. (2011). One shot learning of simple visual concepts. In Proceedings of the 33rd Annual Conference of the Cognitive Science Society. [Videos]

Lake, B. M. and McClelland, J. L. (2011). Estimating the strength of unlabeled information during semi-supervised learning. In Proceedings of the 33rd Annual Conference of the Cognitive Science Society.

Lake, B. M. and Tenenbaum, J. B. (2010). Discovering Structure by Learning Sparse Graphs. In Proceedings of the 32nd Annual Conference of the Cognitive Science Society.

Lake, B. M. (2009). Unsupervised and semi-supervised perceptual category learning. Master’s thesis, Stanford University.

Lake, B. M., Vallabha, G. K., and McClelland, J. L. (2009). Modeling unsupervised perceptual category learning. IEEE Transactions on Autonomous Mental Development, 1(1), 35-43.

Lake, B. M., Vallabha, G. K., and McClelland, J. L. (2008). Modeling unsupervised perceptual category learning. In Proceedings of the 7th International Conference on Development and Learning.

Lake, B. M. and Cottrell, G.W. (2005). Age of acquisition in facial identification: A connectionist approach. In Proceedings of the 27th Annual Conference of the Cognitive Science Society.