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05/30/2023
Compositional diversity in visual concept learning
Humans leverage compositionality to efficiently learn new concepts, unde...
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05/24/2023
What can generic neural networks learn from a child's visual experience?
Young children develop sophisticated internal models of the world based ...
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02/16/2022
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines
Early in development, children learn to extend novel category labels to ...
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07/06/2021
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning
Human reasoning can often be understood as an interplay between two syst...
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05/20/2021
Flexible Compositional Learning of Structured Visual Concepts
Humans are highly efficient learners, with the ability to grasp the mean...
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03/10/2021
Fast and flexible: Human program induction in abstract reasoning tasks
The Abstraction and Reasoning Corpus (ARC) is a challenging program indu...
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02/23/2021
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
To achieve human-like common sense about everyday life, machine learning...
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08/04/2020
Word meaning in minds and machines
Machines show an increasingly broad set of linguistic competencies, than...
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07/31/2020
Self-supervised learning through the eyes of a child
Within months of birth, children have meaningful expectations about the ...
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06/25/2020
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
A hallmark of human intelligence is the ability to interact directly wit...
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03/19/2020
Generating new concepts with hybrid neuro-symbolic models
Human conceptual knowledge supports the ability to generate novel yet hi...
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03/12/2020
Learning word-referent mappings and concepts from raw inputs
How do children learn correspondences between the language and the world...
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03/12/2020
Learning Compositional Rules via Neural Program Synthesis
Many aspects of human reasoning, including language, require learning ru...
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03/11/2020
A Benchmark for Systematic Generalization in Grounded Language Understanding
Human language users easily interpret expressions that describe unfamili...
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02/16/2020
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
We explore the benefits of augmenting state-of-the-art model-free deep r...
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07/23/2019
Modeling question asking using neural program generation
People ask questions that are far richer, more informative, and more cre...
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06/24/2019
Mutual exclusivity as a challenge for neural networks
Strong inductive biases allow children to learn in fast and adaptable wa...
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06/20/2019
Improving the robustness of ImageNet classifiers using elements of human visual cognition
We investigate the robustness properties of image recognition models equ...
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06/12/2019
Compositional generalization through meta sequence-to-sequence learning
People can learn a new concept and use it compositionally, understanding...
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04/17/2019
People infer recursive visual concepts from just a few examples
Machine learning has made major advances in categorizing objects in imag...
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03/05/2019
Learning a smooth kernel regularizer for convolutional neural networks
Modern deep neural networks require a tremendous amount of data to train...
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02/09/2019
The Omniglot Challenge: A 3-Year Progress Report
Three years ago, we released the Omniglot dataset for developing more hu...
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01/14/2019
Human few-shot learning of compositional instructions
People learn in fast and flexible ways that have not been emulated by ma...
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07/19/2018
Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks
Systematic compositionality is the ability to recombine meaningful units...
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02/08/2018
Learning Inductive Biases with Simple Neural Networks
People use rich prior knowledge about the world in order to efficiently ...
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11/16/2017
Question Asking as Program Generation
A hallmark of human intelligence is the ability to ask rich, creative, a...
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10/31/2017
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...
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04/01/2016