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08/29/2023
Policy composition in reinforcement learning via multi-objective policy optimization
We enable reinforcement learning agents to learn successful behavior pol...
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03/31/2023
Accelerating exploration and representation learning with offline pre-training
Sequential decision-making agents struggle with long horizon tasks, sinc...
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12/20/2021
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Traditional automated theorem provers for first-order logic depend on sp...
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03/05/2021
Training a First-Order Theorem Prover from Synthetic Data
A major challenge in applying machine learning to automated theorem prov...
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10/04/2020
Learning Compositional Structures for Deep Learning: Why Routing-by-agreement is Necessary
A formal description of the compositionality of neural networks is assoc...
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06/19/2020
Learning to Prove from Synthetic Theorems
A major challenge in applying machine learning to automated theorem prov...
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05/16/2020
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition
Modern Automatic Speech Recognition (ASR) technology has evolved to iden...
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07/02/2018
Block-Value Symmetries in Probabilistic Graphical Models
Several lifted inference algorithms for probabilistic graphical models f...
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07/27/2017
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Lifted inference algorithms commonly exploit symmetries in a probabilist...
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07/22/2017
Coarse-to-Fine Lifted MAP Inference in Computer Vision
There is a vast body of theoretical research on lifted inference in prob...
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06/30/2016