Gaussian Process Dynamical Models

By Jack M. Wang, David J. Fleet, Aaron Hertzmann. Neural Information Processing Systems, 2005

593 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/888dc8dd3a0a10d4b75c6f131cbeb2af33755e07

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Citations in the corpus

Variational Gaussian Process Dynamical Systems (2011, background): “[5, 3]) marginalise out only F and seek a MAP solution for X .”
Dynamic Factor Graphs for Time Series Modeling (2009, background): “More recently, Gaussian Processes with dynamics on latent variables have been introduced [20], but they suﬀer from a quadratic dependence on the number of training samples.”
HP-GAN: Probabilistic 3D Human Motion Prediction via GAN (2017, background): “Most work in probabilistic human motion prediction uses non-deep learning approaches [28, 33, 38, 39, 20, 21, 7].”
Learning invariance through imitation (2011, unknown): “[23, 18, 29, 27] to name a few).”
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series (2019, background): “Machine learning has a long history in time series modelling (Mitchell, 1999; Gers et al., 2000; Wang et al., 2006; Chung et al., 2014).”
Learning predictive models of a depth camera & manipulator from raw execution traces (2014, background): “In the machine learning community, where probabilistic models play a central role, the identification problem is typically concerned with learning the parameters of a controlled stochastic process that generated the observations [11], [25], [26].”
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation (2016, background): “Introduction Gaussian Processes (GPs) are powerful nonparametric distributions over continuous functions that are routinely deployed in probabilistic modelling for applications including regression and classification (Rasmussen and Williams, 2005), representation learning (Lawrence, 2005), state spa”
Recurrent Network Models for Human Dynamics (2015, methodology): “[34], c) Gaussian Process Dynamic Model (GPDM) of Wang et al.[44], and d) a nearest neighbor N-gram model (NGRAM).”
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC (2013, unknown): “000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053”
3D Pose from Motion for Cross-View Action Recognition via Non-linear Circulant Temporal Encoding (2014, unknown): “Due to the highly unconstrained nature of this problem, approaches in this field have focused on building statistical priors of motion models [29].”
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models (2008, background): “In related work, Wang et al. (2006) extend GPLVMs by learning a dynamics model in latent space.”
Bayesian Gaussian Process Latent Variable Model (2010, unknown): “This could be a Kalman filter, a general Gaussian process (Lawrence and Moore, 2007) or an auto regressive Gaussian process (Wang et al., 2006).”
Gaussian Processes for Regression and Optimisation (2007, methodology): “Interesting machine learning applications include Gaussian processes for reinforcement learning [60], the incorporation of derivative observations into Gaussian process models [73], Gaussian processes to speed up the evaluation of Bayesian integrals [58], and Gaussian process models of dynamical sys”
Efficient high dimensional maximum entropy modeling via symmetric partition functions (2012, unknown): “Also notable are latent variable models that employ Gaussian process regression to probabilistically represent observation models and the latent dynamics [14, 10, 7].”