Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

41 / 141 papers shown

Title
Learning Deep Kernels for Non-Parametric Two-Sample Tests Feng Liu Wenkai Xu Jie Lu Guangquan Zhang A. Gretton Danica J. Sutherland 16 176 0 21 Feb 2020
Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows G. Yoo H. Owhadi 22 21 0 19 Feb 2020
Being Bayesian about Categorical Probability Taejong Joo U. Chung Minji Seo UQCV BDL 22 58 0 19 Feb 2020
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications Yun Yuan X. Yang Zhao Zhang Shandian Zhe AI4CE 18 94 0 06 Feb 2020
Approximate Inference for Fully Bayesian Gaussian Process Regression V. Lalchand C. Rasmussen GP 17 51 0 31 Dec 2019
Randomly Projected Additive Gaussian Processes for Regression Ian A. Delbridge D. Bindel A. Wilson 11 27 0 30 Dec 2019
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes Greg Yang 33 189 0 28 Oct 2019
Neural Spectrum Alignment: Empirical Study Dmitry Kopitkov Vadim Indelman 16 14 0 19 Oct 2019
Recurrent Attentive Neural Process for Sequential Data Shenghao Qin Jiacheng Zhu Jimmy Qin Wenshuo Wang Ding Zhao BDL AI4TS 16 38 0 17 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 19 93 0 14 Oct 2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes Arnu Pretorius Herman Kamper Steve Kroon 16 9 0 12 Oct 2019
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods Kevin J Liang Guoyin Wang Yitong Li Ricardo Henao Lawrence Carin 18 2 0 09 Oct 2019
Deep Kernel Learning for Clustering Chieh-Tsai Wu Zulqarnain Khan Yale Chang Stratis Ioannidis Jennifer Dy 90 13 0 09 Aug 2019
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 24 141 0 17 Jul 2019
GP-VAE: Deep Probabilistic Time Series Imputation Vincent Fortuin Dmitry Baranchuk Gunnar Rätsch Stephan Mandt BDL AI4TS 17 245 0 09 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 13 0 0 03 Jul 2019
The Functional Neural Process Christos Louizos Xiahan Shi Klamer Schutte Max Welling BDL 21 77 0 19 Jun 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak J. Gardner UQCV BDL 27 3 0 31 May 2019
Adaptive Deep Kernel Learning Prudencio Tossou Basile Dura François Laviolette M. Marchand Alexandre Lacoste 19 29 0 28 May 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 24 899 0 26 Apr 2019
Attentive Neural Processes Hyunjik Kim A. Mnih Jonathan Richard Schwarz M. Garnelo S. M. Ali Eslami Dan Rosenbaum Oriol Vinyals Yee Whye Teh 21 429 0 17 Jan 2019
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach C. Lataniotis S. Marelli Bruno Sudret 15 66 0 15 Dec 2018
Neural Non-Stationary Spectral Kernel Sami Remes Markus Heinonen Samuel Kaski BDL 14 9 0 27 Nov 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 16 306 0 11 Oct 2018
Kernel Flows: from learning kernels from data into the abyss H. Owhadi G. Yoo 13 88 0 13 Aug 2018
Stagewise Safe Bayesian Optimization with Gaussian Processes Yanan Sui Vincent Zhuang J. W. Burdick Yisong Yue 13 139 0 20 Jun 2018
Differentiable Compositional Kernel Learning for Gaussian Processes Shengyang Sun Guodong Zhang Chaoqi Wang Wenyuan Zeng Jiaman Li Roger C. Grosse BDL 13 69 0 12 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 39 951 0 05 Jun 2018
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression Haitao Liu Jianfei Cai Yi Wang Yew-Soon Ong 14 83 0 03 Jun 2018
Deep Embedding Kernel Linh Le Ying Xie 19 10 0 16 Apr 2018
Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss J. Gardner Kilian Q. Weinberger A. Wilson 17 94 0 16 Mar 2018
Deep Expander Networks: Efficient Deep Networks from Graph Theory Ameya Prabhu G. Varma A. Namboodiri GNN 30 70 0 23 Nov 2017
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 19 1,068 0 01 Nov 2017
Auto-Differentiating Linear Algebra Matthias Seeger A. Hetzel Zhenwen Dai Eric Meissner Neil D. Lawrence 17 38 0 24 Oct 2017
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition Pavel Izmailov Alexander Novikov D. Kropotov 15 61 0 19 Oct 2017
Contextual Explanation Networks Maruan Al-Shedivat Kumar Avinava Dubey Eric P. Xing CML 24 82 0 29 May 2017
Deep Radial Kernel Networks: Approximating Radially Symmetric Functions with Deep Networks B. McCane Lech Szymanski 31 6 0 09 Mar 2017
Deep Kernelized Autoencoders Michael C. Kampffmeyer Sigurd Løkse F. Bianchi Robert Jenssen L. Livi 13 18 0 08 Feb 2017
Learning Scalable Deep Kernels with Recurrent Structure Maruan Al-Shedivat A. Wilson Yunus Saatchi Zhiting Hu Eric P. Xing BDL 9 104 0 27 Oct 2016
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models K. Krauth Edwin V. Bonilla Kurt Cutajar Maurizio Filippone GP BDL 9 54 0 18 Oct 2016
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 86 271 0 24 Feb 2014