389 citations · 2.9k across the 113 of their papers we have counts for
28 papers · 1 filter
Guided Exploration for Efficient Relational Model Learning
Annie Feng, Nishanth Kumar, Tomas Lozano-Perez +1
Efficient exploration is critical for learning relational models in large-scale environments with complex, long-horizon tasks. Random exploration methods often collect redundant or…
Scaling Exponents Across Parameterizations and Optimizers
Katie Everett, Lechao Xiao, Mitchell Wortsman +8
Robust and effective scaling of models from small to large width typically requires the precise adjustment of many algorithmic and architectural details, such as parameterization a…
Compositional Generative Modeling: A Single Model is Not All You Need
Yilun Du, Leslie Kaelbling
Large monolithic generative models trained on massive amounts of data have become an increasingly dominant approach in AI research. In this paper, we argue that we should instead c…
What Planning Problems Can A Relational Neural Network Solve?
Jiayuan Mao, Tomás Lozano-Pérez, Joshua B. Tenenbaum +1
Goal-conditioned policies are generally understood to be "feed-forward" circuits, in the form of neural networks that map from the current state and the goal specification to the n…
Neural Relational Inference with Fast Modular Meta-learning
Ferran Alet, Erica Weng, Tomás Lozano Pérez +1
\textit{Graph neural networks} (GNNs) are effective models for many dynamical systems consisting of entities and relations. Although most GNN applications assume a single type of e…
Compositional Foundation Models for Hierarchical Planning
Anurag Ajay, Seungwook Han, Yilun Du +7
To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planni…