Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 174 papers shown

Title
Fast Adaptation with Linearized Neural Networks Wesley J. Maddox Shuai Tang Pablo G. Moreno A. Wilson Andreas C. Damianou 32 32 0 02 Mar 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 21 107 0 24 Feb 2021
DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou Moksh Jain Hadi Nekoei V. Butoi Paul Bertin Jarrid Rector-Brooks Maksym Korablyov Yoshua Bengio PER UQLM UQCV UD 204 81 0 16 Feb 2021
Few-Shot Bayesian Optimization with Deep Kernel Surrogates Martin Wistuba Josif Grabocka BDL 37 68 0 19 Jan 2021
Transferring model structure in Bayesian transfer learning for Gaussian process regression Milan Papez A. Quinn 19 11 0 18 Jan 2021
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning Chao Du Zhifeng Gao Shuo Yuan Lining Gao Z. Li Yifan Zeng Xiaoqiang Zhu Jian Xu Kun Gai Kuang-chih Lee 25 18 0 25 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 28 34 0 03 Nov 2020
Are wider nets better given the same number of parameters? A. Golubeva Behnam Neyshabur Guy Gur-Ari 27 44 0 27 Oct 2020
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 21 25 0 26 Oct 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 19 26 0 22 Oct 2020
Few-shot Learning for Spatial Regression Tomoharu Iwata Yusuke Tanaka 30 11 0 09 Oct 2020
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels Alexander Lavin BDL MedIm 8 9 0 16 Sep 2020
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 43 19 0 11 Aug 2020
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake C. Snell R. Zemel 33 63 0 20 Jul 2020
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks Sujay Thakur Cooper Lorsung Yaniv Yacoby Finale Doshi-Velez Weiwei Pan BDL UQCV 30 4 0 21 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
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation Yifan Chen H. Owhadi Andrew M. Stuart 20 31 0 22 May 2020
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks Marius Hobbhahn Agustinus Kristiadi Philipp Hennig BDL UQCV 76 31 0 02 Mar 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 33 277 0 24 Feb 2020
Amortised Learning by Wake-Sleep W. Li Theodore H. Moskovitz Heishiro Kanagawa M. Sahani OOD 23 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 19 176 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 25 58 0 19 Feb 2020
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights Theofanis Karaletsos T. Bui BDL 17 23 0 10 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 34 95 0 06 Feb 2020
Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder Aldo Pacchiano K. Choromanski Stephen J. Roberts 14 158 0 03 Feb 2020
Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders Mohammad Golbabaee Guido Bounincontri Carolin M. Pirkl Marion I. Menzel Bjoern H. Menze Mike Davies Pedro A. Gómez MedIm 29 5 0 23 Jan 2020
Approximate Inference for Fully Bayesian Gaussian Process Regression V. Lalchand C. Rasmussen GP 33 51 0 31 Dec 2019
Randomly Projected Additive Gaussian Processes for Regression Ian A. Delbridge D. Bindel A. Wilson 24 27 0 30 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
Neural Spectrum Alignment: Empirical Study Dmitry Kopitkov Vadim Indelman 29 14 0 19 Oct 2019
Recurrent Attentive Neural Process for Sequential Data Shenghao Qin Jiacheng Zhu Jimmy Qin Wenshuo Wang Ding Zhao BDL AI4TS 27 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 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 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 30 2 0 09 Oct 2019
Deep Kernel Learning via Random Fourier Features Jiaxuan Xie Fanghui Liu Kaijie Wang Xiaolin Huang 9 18 0 07 Oct 2019
Deep Kernel Learning for Clustering Chieh-Tsai Wu Zulqarnain Khan Yale Chang Stratis Ioannidis Jennifer Dy 100 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 38 142 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 29 0 0 03 Jul 2019
The Functional Neural Process Christos Louizos Xiahan Shi Klamer Schutte Max Welling BDL 38 77 0 19 Jun 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 27 3 0 31 May 2019
Adaptive Deep Kernel Learning Prudencio Tossou Basile Dura François Laviolette M. Marchand Alexandre Lacoste 24 29 0 28 May 2019
Kernel Mean Matching for Content Addressability of GANs Wittawat Jitkrittum Patsorn Sangkloy Muhammad Waleed Gondal Amit Raj James Hays Bernhard Schölkopf GAN BDL 26 9 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
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 8 225 0 19 Mar 2019
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach Minyoung Kim Pritish Sahu Behnam Gholami Vladimir Pavlovic OOD 6 45 0 23 Feb 2019
Meta-Learning Mean Functions for Gaussian Processes Vincent Fortuin Heiko Strathmann Gunnar Rätsch BDL FedML MLT 11 29 0 23 Jan 2019
Attentive Neural Processes Hyunjik Kim A. Mnih Jonathan Richard Schwarz M. Garnelo S. M. Ali Eslami Dan Rosenbaum Oriol Vinyals Yee Whye Teh 42 429 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 23 66 0 15 Dec 2018