153 citations · 448 across the 14 of their papers we have counts for
16 papers
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing
Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury +4
Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across…
Learning in Factored Domains with Information-Constrained Visual Representations
Tailia Malloy, Miao Liu, Matthew D. Riemer +3
Humans learn quickly even in tasks that contain complex visual information. This is due in part to the efficient formation of compressed representations of visual information, allo…
Compositional generalization through abstract representations in human and artificial neural networks
Takuya Ito, Tim Klinger, Douglas H. Schultz +3
Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mecha…
Hierarchical Reinforcement Learning with AI Planning Models
Junkyu Lee, Michael Katz, Don Joven Agravante +4
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy to integrat…
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games
Tailia Malloy, Tim Klinger, Miao Liu +3
This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorit…
Deep RL With Information Constrained Policies: Generalization in Continuous Control
Tailia Malloy, Chris R. Sims, Tim Klinger +3
Biological agents learn and act intelligently in spite of a highly limited capacity to process and store information. Many real-world problems involve continuous control, which rep…