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09/12/2023
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract con...
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09/01/2023
Quantum Cognitive Modeling: New Applications and Systems Research Directions
Expanding the benefits of quantum computing to new domains remains a cha...
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07/14/2023
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks
A key property of neural networks (both biological and artificial) is ho...
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06/04/2023
Systematic Visual Reasoning through Object-Centric Relational Abstraction
Human visual reasoning is characterized by an ability to identify abstra...
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03/03/2023
Learning to reason over visual objects
A core component of human intelligence is the ability to identify abstra...
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05/23/2022
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Strong inductive biases are a key component of human intelligence, allow...
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04/13/2022
Modularity benefits reinforcement learning agents with competing homeostatic drives
The problem of balancing conflicting needs is fundamental to intelligenc...
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04/04/2022
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
The ability to acquire abstract knowledge is a hallmark of human intelli...
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10/28/2021
Distill: Domain-Specific Compilation for Cognitive Models
This paper discusses our proposal and implementation of Distill, a domai...
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06/14/2021
A Self-Supervised Framework for Function Learning and Extrapolation
Understanding how agents learn to generalize – and, in particular, to ex...
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05/14/2021
Control of mental representations in human planning
One of the most striking features of human cognition is the capacity to ...
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12/29/2020
Emergent Symbols through Binding in External Memory
A key aspect of human intelligence is the ability to infer abstract rule...
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12/13/2020
A Memory-Augmented Neural Network Model of Abstract Rule Learning
Human intelligence is characterized by a remarkable ability to infer abs...
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10/05/2020
Meta-Learning of Compositional Task Distributions in Humans and Machines
Modern machine learning systems struggle with sample efficiency and are ...
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07/20/2020
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
The terms multi-task learning and multitasking are easily confused. Mult...
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07/09/2020
Learning Representations that Support Extrapolation
Extrapolation – the ability to make inferences that go beyond the scope ...
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02/13/2020
The Efficiency of Human Cognition Reflects Planned Information Processing
Planning is useful. It lets people take actions that have desirable long...
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10/15/2019
Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora
Understanding how human semantic knowledge is organized and how people u...
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09/08/2018
Multitasking Capacity: Hardness Results and Improved Constructions
We consider the problem of determining the maximal α∈ (0,1] such that ev...
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11/08/2017