Brenden M. Lake

Word meaning in minds and machines

Machines show an increasingly broad set of linguistic competencies, than...
08/04/2020 ∙ by Brenden M. Lake, et al. ∙ 0 ∙ share

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Self-supervised learning through the eyes of a child

Within months of birth, children have meaningful expectations about the ...
07/31/2020 ∙ by A. Emin Orhan, et al. ∙ 0 ∙ share

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Learning Task-General Representations with Generative Neuro-Symbolic Modeling

A hallmark of human intelligence is the ability to interact directly wit...
06/25/2020 ∙ by Reuben Feinman, et al. ∙ 5 ∙ share

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Generating new concepts with hybrid neuro-symbolic models

Human conceptual knowledge supports the ability to generate novel yet hi...
03/19/2020 ∙ by Reuben Feinman, et al. ∙ 14 ∙ share

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Learning word-referent mappings and concepts from raw inputs

How do children learn correspondences between the language and the world...
03/12/2020 ∙ by Wai Keen Vong, et al. ∙ 0 ∙ share

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Learning Compositional Rules via Neural Program Synthesis

Many aspects of human reasoning, including language, require learning ru...
03/12/2020 ∙ by Maxwell I. Nye, et al. ∙ 13 ∙ share

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A Benchmark for Systematic Generalization in Grounded Language Understanding

Human language users easily interpret expressions that describe unfamili...
03/11/2020 ∙ by Laura Ruis, et al. ∙ 8 ∙ share

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Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning

We explore the benefits of augmenting state-of-the-art model-free deep r...
02/16/2020 ∙ by Guy Davidson, et al. ∙ 0 ∙ share

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Modeling question asking using neural program generation

People ask questions that are far richer, more informative, and more cre...
07/23/2019 ∙ by ZiYun Wang, et al. ∙ 0 ∙ share

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Mutual exclusivity as a challenge for neural networks

Strong inductive biases allow children to learn in fast and adaptable wa...
06/24/2019 ∙ by Kanishk Gandhi, et al. ∙ 0 ∙ share

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Improving the robustness of ImageNet classifiers using elements of human visual cognition

We investigate the robustness properties of image recognition models equ...
06/20/2019 ∙ by A. Emin Orhan, et al. ∙ 2 ∙ share

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Compositional generalization through meta sequence-to-sequence learning

People can learn a new concept and use it compositionally, understanding...
06/12/2019 ∙ by Brenden M. Lake, et al. ∙ 0 ∙ share

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People infer recursive visual concepts from just a few examples

Machine learning has made major advances in categorizing objects in imag...
04/17/2019 ∙ by Brenden M. Lake, et al. ∙ 0 ∙ share

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Learning a smooth kernel regularizer for convolutional neural networks

Modern deep neural networks require a tremendous amount of data to train...
03/05/2019 ∙ by Reuben Feinman, et al. ∙ 0 ∙ share

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The Omniglot Challenge: A 3-Year Progress Report

Three years ago, we released the Omniglot dataset for developing more hu...
02/09/2019 ∙ by Brenden M. Lake, et al. ∙ 6 ∙ share

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Human few-shot learning of compositional instructions

People learn in fast and flexible ways that have not been emulated by ma...
01/14/2019 ∙ by Brenden M. Lake, et al. ∙ 0 ∙ share

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Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks

Systematic compositionality is the ability to recombine meaningful units...
07/19/2018 ∙ by João Loula, et al. ∙ 0 ∙ share

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Learning Inductive Biases with Simple Neural Networks

People use rich prior knowledge about the world in order to efficiently ...
02/08/2018 ∙ by Reuben Feinman, et al. ∙ 0 ∙ share

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Question Asking as Program Generation

A hallmark of human intelligence is the ability to ask rich, creative, a...
11/16/2017 ∙ by Anselm Rothe, et al. ∙ 0 ∙ share

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Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks

Humans can understand and produce new utterances effortlessly, thanks to...
10/31/2017 ∙ by Brenden M. Lake, et al. ∙ 0 ∙ share

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Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in ...
04/01/2016 ∙ by Brenden M. Lake, et al. ∙ 0 ∙ share

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Brenden M. Lake

Featured Co-authors

Ruslan Salakhutdinov

Joshua B. Tenenbaum

Marco Baroni

Tal Linzen

Armando Solar-Lezama

Jacob Andreas

ZiYun Wang

Diane Bouchacourt

Samuel J. Gershman

Reuben Feinman

A. Emin Orhan

Word meaning in minds and machines

Self-supervised learning through the eyes of a child

Learning Task-General Representations with Generative Neuro-Symbolic Modeling

Generating new concepts with hybrid neuro-symbolic models

Learning word-referent mappings and concepts from raw inputs

Learning Compositional Rules via Neural Program Synthesis

A Benchmark for Systematic Generalization in Grounded Language Understanding

Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning

Modeling question asking using neural program generation

Mutual exclusivity as a challenge for neural networks

Improving the robustness of ImageNet classifiers using elements of human visual cognition

Compositional generalization through meta sequence-to-sequence learning

People infer recursive visual concepts from just a few examples

Learning a smooth kernel regularizer for convolutional neural networks

The Omniglot Challenge: A 3-Year Progress Report

Human few-shot learning of compositional instructions

Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks

Learning Inductive Biases with Simple Neural Networks

Question Asking as Program Generation

Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks

Building Machines That Learn and Think Like People