暂无分享,去创建一个
James Hensman | Stefanos Eleftheriadis | Vincent Adam | Nicolas Durrande | Artem Artemev | J. Hensman | N. Durrande | Vincent Adam | A. Artemev | Stefanos Eleftheriadis
[1] Neil D. Lawrence,et al. Parallelizable sparse inverse formulation Gaussian processes (SpInGP) , 2016, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).
[2] Carl E. Rasmussen,et al. Understanding Probabilistic Sparse Gaussian Process Approximations , 2016, NIPS.
[3] Arno Solin,et al. Infinite-Horizon Gaussian Processes , 2018, NeurIPS.
[4] Simo Särkkä,et al. On convergence and accuracy of state-space approximations of squared exponential covariance functions , 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
[5] Aníbal R. Figueiras-Vidal,et al. Inter-domain Gaussian Processes for Sparse Inference using Inducing Features , 2009, NIPS.
[6] Thomas A. Runkler,et al. Bayesian Alignments of Warped Multi-Output Gaussian Processes , 2018, NeurIPS.
[7] Arno Solin,et al. Explicit Link Between Periodic Covariance Functions and State Space Models , 2014, AISTATS.
[8] James Hensman,et al. Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era , 2019, AISTATS.
[9] Arno Solin,et al. Applied Stochastic Differential Equations , 2019 .
[10] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[11] Marc Peter Deisenroth,et al. Doubly Stochastic Variational Inference for Deep Gaussian Processes , 2017, NIPS.
[12] M. Seeger. Low Rank Updates for the Cholesky Decomposition , 2004 .
[13] James Hensman,et al. Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).
[14] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[15] Carl E. Rasmussen,et al. Convolutional Gaussian Processes , 2017, NIPS.
[16] Giovanni Pistone,et al. Information Geometry of the Gaussian Distribution in View of Stochastic Optimization , 2015, FOGA.
[17] Carl E. Rasmussen,et al. Rates of Convergence for Sparse Variational Gaussian Process Regression , 2019, ICML.
[18] Manfred Opper,et al. The Variational Gaussian Approximation Revisited , 2009, Neural Computation.
[19] Arno Solin,et al. Variational Fourier Features for Gaussian Processes , 2016, J. Mach. Learn. Res..
[20] Arno Solin,et al. State Space Gaussian Processes with Non-Gaussian Likelihood , 2018, ICML.
[21] James Hensman,et al. Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models , 2018, AISTATS.
[22] พงศ์ศักดิ์ บินสมประสงค์,et al. FORMATION OF A SPARSE BUS IMPEDANCE MATRIX AND ITS APPLICATION TO SHORT CIRCUIT STUDY , 1980 .
[23] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[24] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[25] Neil D. Lawrence,et al. Sparse Convolved Gaussian Processes for Multi-output Regression , 2008, NIPS.
[26] M. Giles. Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation , 2008 .
[27] James Hensman,et al. On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes , 2015, AISTATS.
[28] Neil D. Lawrence,et al. Gaussian Processes for Big Data , 2013, UAI.
[29] Neil D. Lawrence,et al. Deep Gaussian Processes , 2012, AISTATS.
[30] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[31] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[32] Ieva Kazlauskaite,et al. Compositional uncertainty in deep Gaussian processes , 2020, UAI.