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09/20/2023
Hierarchical reinforcement learning with natural language subgoals
Hierarchical reinforcement learning has been a compelling approach for a...
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09/01/2023
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Shannon, in his seminal paper introducing information theory, divided th...
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05/25/2023
Passive learning of active causal strategies in agents and language models
What can be learned about causality and experimentation from passive dat...
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04/12/2023
Meta-Learned Models of Cognition
Meta-learning is a framework for learning learning algorithms through re...
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02/01/2023
Collaborating with language models for embodied reasoning
Reasoning in a complex and ambiguous environment is a key goal for Reinf...
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01/29/2023
Distilling Internet-Scale Vision-Language Models into Embodied Agents
Instruction-following agents must ground language into their observation...
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10/31/2022
Learning to Navigate Wikipedia by Taking Random Walks
A fundamental ability of an intelligent web-based agent is seeking out a...
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10/11/2022
Transformers generalize differently from information stored in context vs in weights
Transformer models can use two fundamentally different kinds of informat...
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07/14/2022
Language models show human-like content effects on reasoning
Abstract reasoning is a key ability for an intelligent system. Large lan...
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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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04/05/2022
Can language models learn from explanations in context?
Large language models can perform new tasks by adapting to a few in-cont...
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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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03/28/2022
An Information Centric Framework for Weather Sensing Data
Weather sensing and forecasting has become increasingly accurate in the ...
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12/07/2021
Tell me why! – Explanations support learning of relational and causal structure
Explanations play a considerable role in human learning, especially in a...
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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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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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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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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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09/12/2019
Analyzing machine-learned representations: A natural language case study
As modern deep networks become more complex, and get closer to human-lik...
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01/23/2019
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a ...
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02/12/2018
