84 citations · 339 across the 34 of their papers we have counts for
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Learning and Inference in Hilbert Space with Quantum Graphical Models
Siddarth Srinivasan, Carlton Downey, Byron Boots
Quantum Graphical Models (QGMs) generalize classical graphical models by adopting the formalism for reasoning about uncertainty from quantum mechanics. Unlike classical graphical m…
Orthogonally Decoupled Variational Gaussian Processes
Hugh Salimbeni, Ching-An Cheng, Byron Boots +1
Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities h…
Variational Inference for Gaussian Process Models with Linear Complexity
Ching-An Cheng, Byron Boots
Large-scale Gaussian process inference has long faced practical challenges due to time and space complexity that is superlinear in dataset size. While sparse variational Gaussian p…
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 Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman, Nicholas Rhinehart, Wen Sun +5
Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement trai…
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…