375 citations · 1.9k across the 50 of their papers we have counts for
8 papers · 1 filter
Learning Beam Search Policies via Imitation Learning
Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon
Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do…
Recurrent Predictive State Policy Networks
Ahmed Hefny, Zita Marinho, Wen Sun +2
We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent architecture that brings insights from predictive state representations to reinforcement learning in par…
Learning Hidden Quantum Markov Models
Siddarth Srinivasan, Geoff Gordon, Byron Boots
Hidden Quantum Markov Models (HQMMs) can be thought of as quantum probabilistic graphical models that can model sequential data. We extend previous work on HQMMs with three contrib…
Predictive State Recurrent Neural Networks
Carlton Downey, Ahmed Hefny, Boyue Li +2
We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural N…
DeepArchitect: Automatically Designing and Training Deep Architectures
Renato Negrinho, Geoff Gordon
In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization f…
Practical Learning of Predictive State Representations
Carlton Downey, Ahmed Hefny, Geoffrey Gordon
Over the past decade there has been considerable interest in spectral algorithms for learning Predictive State Representations (PSRs). Spectral algorithms have appealing theoretica…