Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Fast Deep Mixtures of Gaussian Process Experts Clement Etienam K. Law S. Wade Vitaly Zankin 60 2 0 11 Jun 2020
Revisiting Explicit Regularization in Neural Networks for Well-Calibrated Predictive Uncertainty Taejong Joo U. Chung BDL UQCV 32 0 0 11 Jun 2020
Variational Auto-Regressive Gaussian Processes for Continual Learning Sanyam Kapoor Theofanis Karaletsos T. Bui BDL 83 26 0 09 Jun 2020
Physics Informed Deep Kernel Learning Ziyi Wang Wei W. Xing Robert M. Kirby Shandian Zhe PINN 63 10 0 08 Jun 2020
Optimal Transport Graph Neural Networks Benson Chen Gary Bécigneul O. Ganea Regina Barzilay Tommi Jaakkola OT 94 44 0 08 Jun 2020
Longitudinal Deep Kernel Gaussian Process Regression Junjie Liang Yanting Wu Dongkuan Xu Vasant Honavar BDL 21 8 0 24 May 2020
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation Yifan Chen H. Owhadi Andrew M. Stuart 114 31 0 22 May 2020
Deep Latent-Variable Kernel Learning Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang BDL 59 8 0 18 May 2020
How Good are Low-Rank Approximations in Gaussian Process Regression? C. Daskalakis P. Dellaportas A. Panos 62 3 0 03 Apr 2020
Advances in Bayesian Probabilistic Modeling for Industrial Applications Sayan Ghosh Piyush Pandita Steven Atkinson W. Subber Yiming Zhang Natarajan Chennimalai-Kumar S. Chakrabarti Liping Wang AI4CE 39 30 0 26 Mar 2020
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records Yikuan Li Shishir Rao A. Hassaine R. Ramakrishnan Yajie Zhu D. Canoy G. Salimi-Khorshidi Thomas Lukasiewicz K. Rahimi BDL UQCV 76 36 0 23 Mar 2020
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Feng Yin Zhidi Lin Yue Xu Qinglei Kong Deshi Li Sergios Theodoridis Shuguang Cui Cui FedML 142 4 0 08 Mar 2020
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi Markus Heinonen Edwin V. Bonilla Zheyan Shen Maurizio Filippone UQCV BDL 47 0 0 06 Mar 2020
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks Marius Hobbhahn Agustinus Kristiadi Philipp Hennig BDL UQCV 160 34 0 02 Mar 2020
Convolutional Spectral Kernel Learning Jian Li Yong Liu Weiping Wang BDL 29 5 0 28 Feb 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 90 290 0 24 Feb 2020
Amortised Learning by Wake-Sleep W. Li Theodore H. Moskovitz Heishiro Kanagawa M. Sahani OOD 102 7 0 22 Feb 2020
Learning Deep Kernels for Non-Parametric Two-Sample Tests Feng Liu Wenkai Xu Jie Lu Guangquan Zhang Arthur Gretton Danica J. Sutherland 92 189 0 21 Feb 2020
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes Seokhyun Chung Raed Al Kontar Zhenke Wu 35 5 0 19 Feb 2020
Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows G. Yoo H. Owhadi 74 21 0 19 Feb 2020
Being Bayesian about Categorical Probability Taejong Joo U. Chung Minji Seo UQCV BDL 97 61 0 19 Feb 2020
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees Jonas Rothfuss Vincent Fortuin Martin Josifoski Andreas Krause UQCV 93 127 0 13 Feb 2020
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights Theofanis Karaletsos T. Bui BDL 107 24 0 10 Feb 2020
Conditional Deep Gaussian Processes: multi-fidelity kernel learning Chi-Ken Lu Patrick Shafto 58 5 0 07 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 86 98 0 06 Feb 2020
Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder Aldo Pacchiano K. Choromanski Stephen J. Roberts 130 165 0 03 Feb 2020
The Case for Bayesian Deep Learning A. Wilson UQCV BDL OOD 132 114 0 29 Jan 2020
Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders Mohammad Golbabaee Guido Bounincontri Carolin M. Pirkl Marion I. Menzel Bjoern Menze Mike Davies Pedro A. Gómez MedIm 50 5 0 23 Jan 2020
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation N. Brosse C. Riquelme Alice Martin Sylvain Gelly Eric Moulines BDL OOD UQCV 97 34 0 22 Jan 2020
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning Yao Zhang Daniel Jarrett M. Schaar 72 9 0 12 Jan 2020
Approximate Inference for Fully Bayesian Gaussian Process Regression V. Lalchand C. Rasmussen GP 144 52 0 31 Dec 2019
Randomly Projected Additive Gaussian Processes for Regression Ian A. Delbridge D. Bindel A. Wilson 65 27 0 30 Dec 2019
Learning to Impute: A General Framework for Semi-supervised Learning Wei-Hong Li Chuan-Sheng Foo Hakan Bilen SSL 73 10 0 22 Dec 2019
Totally Deep Support Vector Machines H. Sahbi 32 2 0 12 Dec 2019
MetaFun: Meta-Learning with Iterative Functional Updates Jin Xu Jean-François Ton Hyunjik Kim Adam R. Kosiorek Yee Whye Teh 75 68 0 05 Dec 2019
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks Jack K. Fitzsimons Sebastian M. Schmon Stephen J. Roberts BDL FedML 30 0 0 02 Dec 2019
Deep Networks with Adaptive Nyström Approximation Luc Giffon Stéphane Ayache Thierry Artières Hachem Kadri 44 3 0 29 Nov 2019
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints Sam Daulton Shaun Singh Vashist Avadhanula Drew Dimmery E. Bakshy 71 13 0 02 Nov 2019
Function-Space Distributions over Kernels Gregory W. Benton Wesley J. Maddox Jayson Salkey J. Albinati A. Wilson BDL GP 53 26 0 29 Oct 2019
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes Greg Yang 168 202 0 28 Oct 2019
Neural Spectrum Alignment: Empirical Study Dmitry Kopitkov Vadim Indelman 88 14 0 19 Oct 2019
Recurrent Attentive Neural Process for Sequential Data Shenghao Qin Jiacheng Zhu Jimmy Qin Wenshuo Wang Ding Zhao BDL AI4TS 81 38 0 17 Oct 2019
Parametric Gaussian Process Regressors M. Jankowiak Geoffrey Pleiss Jacob R. Gardner UQCV 56 5 0 16 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 75 93 0 14 Oct 2019
Deep Kernels with Probabilistic Embeddings for Small-Data Learning Ankur Mallick Chaitanya Dwivedi B. Kailkhura Gauri Joshi T. Y. Han BDL UQCV 44 8 0 13 Oct 2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes Arnu Pretorius Herman Kamper Steve Kroon 66 9 0 12 Oct 2019
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels Massimiliano Patacchiola Jack Turner Elliot J. Crowley Michael F. P. O'Boyle Amos Storkey BDL 84 19 0 11 Oct 2019
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods Kevin J. Liang Guoyin Wang Yitong Li Ricardo Henao Lawrence Carin 66 2 0 09 Oct 2019
Deep Kernel Learning via Random Fourier Features Jiaxuan Xie Fanghui Liu Kaijie Wang Xiaolin Huang 46 19 0 07 Oct 2019
Deep Message Passing on Sets Yifeng Shi Junier Oliva Marc Niethammer PINN 38 9 0 21 Sep 2019