Deep Gaussian Processes

2 November 2012

Papers citing "Deep Gaussian Processes"

50 / 191 papers shown

Title
Sparse Gaussian Process Variational Autoencoders Matthew Ashman Jonathan So Will Tebbutt Vincent Fortuin Michael Pearce Richard Turner 24 33 0 20 Oct 2020
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings John Mitros A. Pakrashi Brian Mac Namee UQCV 26 2 0 03 Sep 2020
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling Nishant Yadav S. Ravela A. Ganguly OOD AI4Cl AI4CE 16 3 0 12 Aug 2020
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 43 19 0 11 Aug 2020
Blind hierarchical deconvolution Arttu Arjas L. Roininen M. Sillanpää A. Hauptmann 18 4 0 22 Jul 2020
Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion M. Emzir Sari Lasanen Z. Purisha L. Roininen Simo Särkkä 22 9 0 28 Jun 2020
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 35 199 0 22 Jun 2020
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev Henri Pesonen Markus Heinonen J. Corander Samuel Kaski GP 26 10 0 18 Jun 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 24 113 0 18 Jun 2020
NP-PROV: Neural Processes with Position-Relevant-Only Variances Xuesong Wang Lina Yao Xianzhi Wang Feiping Nie BDL 26 3 0 15 Jun 2020
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization Andrew Stirn David A. Knowles DRL 18 10 0 08 Jun 2020
Global Optimization of Gaussian processes Artur M. Schweidtmann D. Bongartz D. Grothe Tim Kerkenhoff Xiaopeng Lin J. Najman Alexander Mitsos 10 40 0 21 May 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 26 60 0 17 May 2020
Time Series Forecasting With Deep Learning: A Survey Bryan Lim S. Zohren AI4TS AI4CE 54 1,187 0 28 Apr 2020
Energy-Based Processes for Exchangeable Data Mengjiao Yang Bo Dai H. Dai Dale Schuurmans 22 12 0 17 Mar 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 40 94 0 02 Mar 2020
Time Series Data Augmentation for Deep Learning: A Survey Qingsong Wen Liang Sun Fan Yang Xiaomin Song Jing Gao Xue Wang Huan Xu AI4TS 32 635 0 27 Feb 2020
Graph Convolutional Gaussian Processes For Link Prediction Felix L. Opolka Pietro Lio GNN 27 15 0 11 Feb 2020
Doubly Sparse Variational Gaussian Processes Vincent Adam Stefanos Eleftheriadis N. Durrande A. Artemev J. Hensman 24 24 0 15 Jan 2020
Approximate Inference for Fully Bayesian Gaussian Process Regression V. Lalchand C. Rasmussen GP 35 51 0 31 Dec 2019
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes Greg Yang 33 191 0 28 Oct 2019
Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin Yu Yizhou Chen Zhongxiang Dai K. H. Low Patrick Jaillet 19 42 0 26 Oct 2019
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat Brian Karrer Daniel R. Jiang Sam Daulton Benjamin Letham A. Wilson E. Bakshy 32 93 0 14 Oct 2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes Arnu Pretorius Herman Kamper Steve Kroon 24 9 0 12 Oct 2019
A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Amir Atapour-Abarghouei Stephen Bonner A. Mcgough 24 7 0 19 Aug 2019
Probabilistic Regressor Chains with Monte Carlo Methods Jesse Read Luca Martino BDL UQCV AI4CE LRM 30 11 0 18 Jul 2019
Learning GPLVM with arbitrary kernels using the unscented transformation Daniel Augusto R. M. A. de Souza Diego Mesquita C. L. C. Mattos Joao P. P. Gomes 29 0 0 03 Jul 2019
Compositionally-Warped Gaussian Processes Gonzalo Rios Felipe A. Tobar 14 41 0 23 Jun 2019
Multi-resolution Multi-task Gaussian Processes Oliver Hamelijnck Theodoros Damoulas Kangrui Wang Mark Girolami 28 38 0 19 Jun 2019
The Functional Neural Process Christos Louizos Xiahan Shi Klamer Schutte Max Welling BDL 38 77 0 19 Jun 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 33 30 0 13 Jun 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 29 3 0 31 May 2019
Non-linear Multitask Learning with Deep Gaussian Processes Ayman Boustati Theodoros Damoulas R. Savage BDL 19 5 0 29 May 2019
Graph Convolutional Gaussian Processes Ian Walker Ben Glocker GNN 22 35 0 14 May 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 44 901 0 26 Apr 2019
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN Alireza Koochali P. Schichtel Sheraz Ahmed Andreas Dengel AI4TS 22 72 0 29 Mar 2019
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 10 225 0 19 Mar 2019
Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes Xubo Yue Raed Al Kontar 32 16 0 09 Mar 2019
Input Prioritization for Testing Neural Networks Taejoon Byun Vaibhav Sharma Abhishek Vijayakumar Sanjai Rayadurgam D. Cofer AAML 29 67 0 11 Jan 2019
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach C. Lataniotis S. Marelli Bruno Sudret 23 66 0 15 Dec 2018
Understanding Priors in Bayesian Neural Networks at the Unit Level M. Vladimirova Jakob Verbeek Pablo Mesejo Julyan Arbel BDL UQCV 6 4 0 11 Oct 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak Lechao Xiao Jaehoon Lee Yasaman Bahri Greg Yang Jiri Hron Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 25 306 0 11 Oct 2018
Probabilistic Meta-Representations Of Neural Networks Theofanis Karaletsos Peter Dayan Zoubin Ghahramani BDL 12 27 0 01 Oct 2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 27 397 0 21 Sep 2018
Learning of Tree-Structured Gaussian Graphical Models on Distributed Data under Communication Constraints Mostafa Tavassolipour S. Motahari M. Manzuri-Shalmani 16 8 0 21 Sep 2018
Efficient Global Optimization using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab 19 19 0 11 Sep 2018
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion Steven Atkinson N. Zabaras 16 36 0 11 Jul 2018
Uncertainty in Multitask Transfer Learning Alexandre Lacoste Boris N. Oreshkin Wonchang Chung Thomas Boquet Negar Rostamzadeh David M. Krueger BDL UQCV SSL 24 21 0 20 Jun 2018
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi José Miguel Hernández-Lobato J. J. Murillo-Fuentes BDL 19 96 0 14 Jun 2018
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty Hao Zhou Yunyang Xiong Vikas Singh UQCV BDL 52 4 0 10 Jun 2018