Publications

Lab publications by year: Preprints, 2021, 2020, 2019, 2018, 2017, and earlier.


Preprints

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. Preprint available on arXiv:2107.02794.

Gandhi, K., Stojnic, G., Lake, B. M. and Dillon, M. R. (2021). Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others. Preprint available on arXiv:2102.11938.

Vong, W. K. and Lake, B. M. (2021). Cross-situational word learning with multimodal neural networks. Preprint available on PsyArXiv:udbh2.


2021

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.]


Selected earlier papers

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]

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]

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.]