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Brenden M. Lake
Researcher at New York University
Publications - 79
Citations - 8401
Brenden M. Lake is an academic researcher from New York University. The author has contributed to research in topics: Concept learning & Artificial neural network. The author has an hindex of 21, co-authored 71 publications receiving 6128 citations. Previous affiliations of Brenden M. Lake include Stanford University & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
Human-level concept learning through probabilistic program induction.
TL;DR: A computational model is described that learns in a similar fashion and does so better than current deep learning algorithms and can generate new letters of the alphabet that look “right” as judged by Turing-like tests of the model's output in comparison to what real humans produce.
Journal ArticleDOI
Building machines that learn and think like people.
TL;DR: In this article, a review of recent progress in cognitive science suggests that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it.
Journal Article
One shot learning of simple visual concepts
TL;DR: A generative model of how characters are composed from strokes is introduced, where knowledge from previous characters helps to infer the latent strokes in novel characters, using a massive new dataset of handwritten characters.
Proceedings Article
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden M. Lake,Marco Baroni +1 more
TL;DR: This paper introduces the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences, and tests the zero-shot generalization capabilities of a variety of recurrent neural networks trained on SCAN with sequence-to-sequence methods.
Supplementary Material for Human-level concept learning through probabilistic program induction
TL;DR: The authors presented a computational model that captures human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets, represented as simple programs that best explain observed examples under a Bayesian criterion.