Hugh Salimbeni

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All published works

Action	Title	Year	Authors
+	GPflux: A Library for Deep Gaussian Processes	2021	Vincent Dutordoir Hugh Salimbeni Eric Hambro John Mcleod Felix Leibfried Artem Artemev Mark van der Wilk James Hensman Marc Peter Deisenroth St. John
+	GPflux: A Library for Deep Gaussian Processes	2021	Vincent Dutordoir Hugh Salimbeni Eric Hambro John McLeod Felix Leibfried Artem Artemev Mark van der Wilk James Hensman Marc Peter Deisenroth St. John
+	Stochastic Differential Equations with Variational Wishart Diffusions	2020	Martin Jørgensen Marc Peter Deisenroth Hugh Salimbeni
+	Stochastic Differential Equations with Variational Wishart Diffusions	2020	Martin Jørgensen Marc Peter Deisenroth Hugh Salimbeni
+	Deep Gaussian Processes with Importance-Weighted Variational Inference	2019	Hugh Salimbeni Vincent Dutordoir James Hensman Marc Peter Deisenroth
+	Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models	2018	Hugh Salimbeni Stefanos Eleftheriadis James Hensman
+	Orthogonally Decoupled Variational Gaussian Processes	2018	Hugh Salimbeni Ching-An Cheng Byron Boots Marc Peter Deisenroth
+	Gaussian Process Conditional Density Estimation	2018	Vincent Dutordoir Hugh Salimbeni Marc Peter Deisenroth James Hensman
+	Orthogonally Decoupled Variational Gaussian Processes	2018	Hugh Salimbeni Ching-An Cheng Byron Boots Marc Peter Deisenroth
+	Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models	2018	Hugh Salimbeni Stefanos Eleftheriadis James Hensman
+	Doubly Stochastic Variational Inference for Deep Gaussian Processes	2017	Hugh Salimbeni Marc Peter Deisenroth
+	Doubly Stochastic Variational Inference for Deep Gaussian Processes	2017	Hugh Salimbeni Marc Peter Deisenroth
+	Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders	2016	Nat Dilokthanakul Pedro A. M. Mediano Marta Garnelo Matthew C. H. Lee Hugh Salimbeni Kai Arulkumaran Murray Shanahan

Common Coauthors

Coauthor	Papers Together
Marc Peter Deisenroth	10
James Hensman	6
Vincent Dutordoir	4
Martin Jørgensen	2
St. John	2
Ching-An Cheng	2
Byron Boots	2
Stefanos Eleftheriadis	2
Mark van der Wilk	2
Eric Hambro	2
Felix Leibfried	2
Artem Artemev	1
John Mcleod	1
Marta Garnelo	1
Artem Artemev	1
Nat Dilokthanakul	1
John McLeod	1
Kai Arulkumaran	1
Matthew C. H. Lee	1
Murray Shanahan	1
Pedro A. M. Mediano	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Gaussian processes for Big data	2013	James Hensman Nicolò Fusi Neil D. Lawrence	4
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	4
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+	Stochastic variational inference	2013	Matthew D. Hoffman David M. Blei Chong Wang John Paisley	2
+	TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems	2016	Martı́n Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Gregory S. Corrado Andy Davis Jay B. Dean Matthieu Devin	2
+	Latent Gaussian Process Regression	2017	Erik Bodin Carl Henrik Ek	2
+	Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals	2012	Chunyi Wang Radford M. Neal	2
+	Nonparametric Bayesian Density Modeling with Gaussian Processes	2009	Ryan P. Adams Iain Murray David Mackay	1
+	Searching for exotic particles in high-energy physics with deep learning	2014	Pierre Baldi Peter Sadowski D. Whiteson	1
+	Practical Bayesian Optimization of Machine Learning Algorithms	2012	Jasper Snoek Hugo Larochelle Ryan P. Adams	1
+	Generalised Wishart Processes	2010	Andrew Gordon Wilson Zoubin Ghahramani	1
+	Gaussian Process Volatility Model	2014	Yue Wu José Miguel Hernández-Lobato Zoubin Ghahramani	1
+	Unsupervised Deep Embedding for Clustering Analysis	2015	Junyuan Xie Ross Girshick Ali Farhadi	1
+	Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks	2015	Jost Tobias Springenberg	1
+	Variational Auto-encoded Deep Gaussian Processes	2015	Zhenwen Dai Andreas Damianou Javier González Neil D. Lawrence	1
+	Generating Sentences from a Continuous Space	2015	Samuel R. Bowman Luke Vilnis Oriol Vinyals Andrew M. Dai Rafał Józefowicz Samy Bengio	1
+	Attend, Infer, Repeat: Fast Scene Understanding with Generative Models	2016	S. M. Ali Eslami Nicolas Heess Théophane Weber Yuval Tassa David Szepesvari Koray Kavukcuoglu Geoffrey E. Hinton	1
+	Ladder Variational Autoencoders	2016	Casper Kaae Sønderby Tapani Raiko Lars Maaløe Søren Kaae Sønderby Ole Winther	1
+	Improving Variational Inference with Inverse Autoregressive Flow	2016	Diederik P. Kingma Tim Salimans Rafał Józefowicz Xi Chen Ilya Sutskever Max Welling	1
+	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets	2016	Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel	1
+	Tagger: Deep Unsupervised Perceptual Grouping	2016	Klaus Greff Antti Rasmus Mathias Berglund Tele Hao Jürgen Schmidhuber Harri Valpola	1
+	Composing graphical models with neural networks for structured representations and fast inference	2016	Matthew Johnson David Duvenaud Alexander B. Wiltschko Sandeep Robert Datta Ryan P. Adams	1
+	Stochastic Backpropagation through Mixture Density Distributions	2016	Alex Graves	1
+ PDF Chat	Nested Kriging predictions for datasets with a large number of observations	2017	Didier Rullière Nicolas Durrande François Bachoc Clément Chevalier	1
+	Convolutional Gaussian Processes	2017	Mark van der Wilk Carl Edward Rasmussen James Hensman	1
+	Deep Gaussian Processes with Decoupled Inducing Inputs	2018	Marton Havasi José Miguel Hernández-Lobato Juan José Murillo-Fuentes	1
+	Inference Suboptimality in Variational Autoencoders	2018	Chris Cremer Xuechen Li David Duvenaud	1
+	Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models	2018	Hugh Salimbeni Stefanos Eleftheriadis James Hensman	1
+	Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo	2018	Marton Havasi José Miguel Hernández-Lobato Juan José Murillo-Fuentes	1
+	Bayesian Optimization using Deep Gaussian Processes	2019	Ali Hebbal Loïc Brevault Mathieu Balesdent El‐Ghazali Talbi Nouredine Melab	1
+	Neural ODEs with stochastic vector field mixtures	2019	Niall Twomey Michał Kozłowski Raúl Santos‐Rodríguez	1
+	Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit	2019	Belinda Tzen Maxim Raginsky	1
+	Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise	2019	Xuanqing Liu Tesi Xiao Si Si Cao Qin Sanjiv Kumar Cho‐Jui Hsieh	1
+	Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks	2015	José Miguel Hernández-Lobato Ryan P. Adams	1
+	Variational Dropout and the Local Reparameterization Trick	2015	Diederik P. Kingma Tim Salimans Max Welling	1
+	Gaussian Processes for Big Data	2013	James Hensman Nicolò Fusi Neil D. Lawrence	1
+	Variational Lossy Autoencoder	2016	Xi Chen Diederik P. Kingma Tim Salimans Yan Duan Prafulla Dhariwal John Schulman Ilya Sutskever Pieter Abbeel	1
+	On the Quantitative Analysis of Decoder-Based Generative Models	2016	Yuhuai Wu Yuri Burda Ruslan Salakhutdinov Roger Grosse	1
+	Deep Gaussian Processes for Regression using Approximate Expectation Propagation	2016	Thang D. Bui Daniel Hernández-Lobato Yingzhen Li José Miguel Hernández-Lobato Richard E. Turner	1
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	1
+	Importance Weighted Autoencoders	2015	Yuri Burda Roger Grosse Ruslan Salakhutdinov	1
+	Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives	2019	George Tucker Dieterich Lawson Shixiang Gu Christopher Maddison	1
+	Self-Normalizing Neural Networks	2017	Günter Klambauer Thomas Unterthiner Andreas Mayr Sepp Hochreiter	1
+	Neural ordinary differential equations	2018	Ricky T. Q. Chen Yulia Rubanova Jesse Bettencourt David Duvenaud	1
+	Semi-described and semi-supervised learning with Gaussian processes	2015	Andreas Damianou Neil D. Lawrence	1
+	Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo	2018	Marton Havasi José Miguel Hernández-Lobato Juan José Murillo-Fuentes	1
+	Augmented Neural ODEs	2019	Emilien Dupont Arnaud Doucet Yee Whye Teh	1
+	Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes	2019	Creighton Heaukulani Mark van der Wilk	1
+	Differential Bayesian Neural Nets	2019	Andreas Look Melih Kandemir	1
+	A Framework for Interdomain and Multioutput Gaussian Processes	2020	Mark van der Wilk Vincent Dutordoir St. John Artem Artemev Vincent Adam James Hensman	1