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09/05/2023
Cognitive Architectures for Language Agents
Recent efforts have incorporated large language models (LLMs) with exter...
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06/14/2023
The Universal Law of Generalization Holds for Naturalistic Stimuli
Shepard's universal law of generalization is a remarkable hypothesis abo...
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05/29/2023
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
Understanding which concepts models can and cannot represent has been fu...
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05/26/2023
Im-Promptu: In-Context Composition from Image Prompts
Large language models are few-shot learners that can solve diverse tasks...
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05/24/2023
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks
Humans can learn languages from remarkably little experience. Developing...
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05/17/2023
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Language models are increasingly being deployed for general problem solv...
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03/20/2023
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Object rearrangement is a challenge for embodied agents because solving ...
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02/02/2023
What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models
Understanding the extent to which the perceptual world can be recovered ...
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01/27/2023
Alignment with human representations supports robust few-shot learning
Should we care whether AI systems have representations of the world that...
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11/07/2022
Humans decompose tasks by trading off utility and computational cost
Human behavior emerges from planning over elaborate decompositions of ta...
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11/02/2022
On the Informativeness of Supervision Signals
Learning transferable representations by training a classifier is a well...
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09/29/2022
Analyzing Diffusion as Serial Reproduction
Diffusion models are a class of generative models that learn to synthesi...
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08/15/2022
Bias amplification in experimental social networks is reduced by resampling
Large-scale social networks are thought to contribute to polarization by...
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08/11/2022
Gaussian process surrogate models for neural networks
The lack of insight into deep learning systems hinders their systematic ...
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07/07/2022
Predicting Word Learning in Children from the Performance of Computer Vision Systems
For human children as well as machine learning systems, a key challenge ...
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07/02/2022
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation
Iterative refinement – start with a random guess, then iteratively impro...
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06/16/2022
How to talk so your robot will learn: Instructions, descriptions, and pragmatics
From the earliest years of our lives, humans use language to express our...
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06/08/2022
Words are all you need? Capturing human sensory similarity with textual descriptors
Recent advances in multimodal training use textual descriptions to signi...
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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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04/11/2022
Linguistic communication as (inverse) reward design
Natural language is an intuitive and expressive way to communicate rewar...
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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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02/24/2022
Probing BERT's priors with serial reproduction chains
We can learn as much about language models from what they say as we lear...
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02/09/2022
Predicting Human Similarity Judgments Using Large Language Models
Similarity judgments provide a well-established method for accessing men...
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02/09/2022
Can Humans Do Less-Than-One-Shot Learning?
Being able to learn from small amounts of data is a key characteristic o...
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12/07/2021
A pragmatic account of the weak evidence effect
Language is not only used to inform. We often seek to persuade by arguin...
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10/08/2021
Distinguishing rule- and exemplar-based generalization in learning systems
Despite the increasing scale of datasets in machine learning, generaliza...
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09/01/2021
Cognitive science as a source of forward and inverse models of human decisions for robotics and control
Those designing autonomous systems that interact with humans will invari...
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07/14/2021
Passive attention in artificial neural networks predicts human visual selectivity
Developments in machine learning interpretability techniques over the pa...
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06/28/2021
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
Many transfer problems require re-using previously optimal decisions for...
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05/25/2021
Extending rational models of communication from beliefs to actions
Speakers communicate to influence their partner's beliefs and shape thei...
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05/15/2021
Are Convolutional Neural Networks or Transformers more like human vision?
Modern machine learning models for computer vision exceed humans in accu...
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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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05/13/2021
Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task
There is substantial variability in the expectations that communication ...
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04/12/2021
From partners to populations: A hierarchical Bayesian account of coordination and convention
Languages are powerful solutions to coordination problems: they provide ...
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01/24/2021
Evaluating Models of Robust Word Recognition with Serial Reproduction
Spoken communication occurs in a "noisy channel" characterized by high l...
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12/16/2020
Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation
Successful teaching entails a complex interaction between a teacher and ...
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12/06/2020
Competition in Cross-situational Word Learning: A Computational Study
Children learn word meanings by tapping into the commonalities across di...
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10/05/2020
Investigating representations of verb bias in neural language models
Languages typically provide more than one grammatical construction to ex...
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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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09/30/2020
Learning Rewards from Linguistic Feedback
We explore unconstrained natural language feedback as a learning signal ...
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09/29/2020
Understanding Human Intelligence through Human Limitations
Recent progress in artificial intelligence provides the opportunity to a...
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07/27/2020
Resource-rational Task Decomposition to Minimize Planning Costs
People often plan hierarchically. That is, rather than planning over a m...
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07/17/2020
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior
Traditional models of category learning in psychology focus on represent...
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07/05/2020
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
This paper seeks to establish a framework for directing a society of sim...
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06/29/2020
Universal linguistic inductive biases via meta-learning
How do learners acquire languages from the limited data available to the...
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06/07/2020
Analogy as Nonparametric Bayesian Inference over Relational Systems
Much of human learning and inference can be framed within the computatio...
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05/29/2020
Extracting low-dimensional psychological representations from convolutional neural networks
Deep neural networks are increasingly being used in cognitive modeling a...
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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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02/04/2020
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks
A key property of linguistic conventions is that they hold over an entir...
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10/16/2019