65 citations · 220 across the 17 of their papers we have counts for
20 papers
Transformers meet Neural Algorithmic Reasoners
Wilfried Bounsi, Borja Ibarz, Andrew Dudzik +5
Transformers have revolutionized machine learning with their simple yet effective architecture. Pre-training Transformers on massive text datasets from the Internet has led to unma…
Unifying Grokking and Double Descent
Xander Davies, Lauro Langosco, David Krueger
A principled understanding of generalization in deep learning may require unifying disparate observations under a single conceptual framework. Previous work has studied \emph{grokk…
Investigating the role of model-based learning in exploration and transfer
Jacob Walker, Eszter Vértes, Yazhe Li +4
State of the art reinforcement learning has enabled training agents on tasks of ever increasing complexity. However, the current paradigm tends to favor training agents from scratc…
Towards Understanding How Machines Can Learn Causal Overhypotheses
Eliza Kosoy, David M. Chan, Adrian Liu +7
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature…
Learning Causal Overhypotheses through Exploration in Children and Computational Models
Eliza Kosoy, Adrian Liu, Jasmine Collins +7
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics,…
Physical Design using Differentiable Learned Simulators
Kelsey R. Allen, Tatiana Lopez-Guevara, Kimberly Stachenfeld +4
Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating…
Procedural Generalization by Planning with Self-Supervised World Models
Ankesh Anand, Jacob Walker, Yazhe Li +5
One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks.…
On the role of planning in model-based deep reinforcement learning
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani +7
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learn…
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Eliza Kosoy, Jasmine Collins, David M. Chan +6
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn. In turn, this ear…
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Giambattista Parascandolo, Lars Buesing, Josh Merel +6
Standard planners for sequential decision making (including Monte Carlo planning, tree search, dynamic programming, etc.) are constrained by an implicit sequential planning assumpt…
Levels of Analysis for Machine Learning
Jessica Hamrick, Shakir Mohamed
Machine learning is currently involved in some of the most vigorous debates it has ever seen. Such debates often seem to go around in circles, reaching no conclusion or resolution.…
Combining Q-Learning and Search with Amortized Value Estimates
Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez +4
We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned pri…
Object-oriented state editing for HRL
Victor Bapst, Alvaro Sanchez-Gonzalez, Omar Shams +4
We introduce agents that use object-oriented reasoning to consider alternate states of the world in order to more quickly find solutions to problems. Specifically, a hierarchical c…
Structured agents for physical construction
Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch +4
Physical construction---the ability to compose objects, subject to physical dynamics, to serve some function---is fundamental to human intelligence. We introduce a suite of challen…
Relational inductive bias for physical construction in humans and machines
Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst +4
While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of…
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst +24
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, i…
Generating Plans that Predict Themselves
Jaime F. Fisac, Chang Liu, Jessica B. Hamrick +4
Collaboration requires coordination, and we coordinate by anticipating our teammates' future actions and adapting to their plan. In some cases, our teammates' actions early on can…
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
Chang Liu, Jessica B. Hamrick, Jaime F. Fisac +4
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human coll…
Pragmatic-Pedagogic Value Alignment
Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick +7
As intelligent systems gain autonomy and capability, it becomes vital to ensure that their objectives match those of their human users; this is known as the value-alignment problem…
Metacontrol for Adaptive Imagination-Based Optimization
Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu +3
Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run---especially with respect to the easie…