84 citations · 339 across the 34 of their papers we have counts for
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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…
Manifold Regularization for Kernelized LSTD
Xinyan Yan, Krzysztof Choromanski, Byron Boots +1
Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL). It is a necessary component of policy iteration and can be used f…
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…
One-Shot Learning for Semantic Segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu +2
Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we 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…