Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Probability-Generating Function Kernels for Spherical Data Theodore Papamarkou Alexey Lindo GP 29 0 0 01 Dec 2021
Neural Fields as Learnable Kernels for 3D Reconstruction Francis Williams Zan Gojcic S. Khamis Denis Zorin Joan Bruna Sanja Fidler Or Litany 106 66 0 26 Nov 2021
Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication Franziska Nestler Martin Stoll Theresa Wagner 43 9 0 19 Nov 2021
Multi-Task Neural Processes Jiayi Shen Xiantong Zhen M. Worring Ling Shao BDL 73 8 0 10 Nov 2021
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation Minyoung Kim Timothy M. Hospedales BDL 90 5 0 09 Nov 2021
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes Hugh Dance Brooks Paige GP 67 10 0 08 Nov 2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data Andrew Jesson P. Tigas Joost R. van Amersfoort Andreas Kirsch Uri Shalit Y. Gal CML 109 32 0 03 Nov 2021
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization Tomoharu Iwata BDL 52 4 0 01 Nov 2021
Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan Yalin Wang 33 2 0 30 Oct 2021
Improving Hyperparameter Optimization by Planning Ahead H. Jomaa Jonas K. Falkner Lars Schmidt-Thieme 64 0 0 15 Oct 2021
Analysis of the first Genetic Engineering Attribution Challenge O. Crook K. L. Warmbrod G. Lipstein Christine Chung Christopher W. Bakerlee ... Shelly R. Holland Jacob Swett K. Esvelt E. C. Alley W. Bradshaw 40 9 0 14 Oct 2021
Dense Gaussian Processes for Few-Shot Segmentation Joakim Johnander Johan Edstedt Michael Felsberg Fahad Shahbaz Khan Martin Danelljan 119 30 0 07 Oct 2021
Deep Classifiers with Label Noise Modeling and Distance Awareness Vincent Fortuin Mark Collier F. Wenzel J. Allingham J. Liu Dustin Tran Balaji Lakshminarayanan Jesse Berent Rodolphe Jenatton E. Kokiopoulou UQCV 76 11 0 06 Oct 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 71 4 0 01 Oct 2021
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes Jongseo Lee Jianxiang Feng Matthias Humt M. Müller Rudolph Triebel UQCV 92 21 0 20 Sep 2021
Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time A. Lin Jie Lu Junyu Xuan Fujin Zhu Guangquan Zhang CML 38 0 0 20 Sep 2021
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization Haitao Liu Jiaqi Ding Xinyu Xie Xiaomo Jiang Yusong Zhao Xiaofang Wang BDL 55 17 0 20 Sep 2021
Global Convolutional Neural Processes Xuesong Wang Lina Yao Xianzhi Wang Hye-Young Paik Sen Wang BDL AI4CE 90 5 0 02 Sep 2021
Toward a `Standard Model' of Machine Learning Zhiting Hu Eric Xing 95 12 0 17 Aug 2021
BoA-PTA, A Bayesian Optimization Accelerated Error-Free SPICE Solver W. Xing X. Jin Yi Liu Dan Niu Weisheng Zhao Zhou Jin 28 4 0 31 Jul 2021
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models Zhiliang Wu Yinchong Yang Peter A. Fasching Volker Tresp BDL 73 10 0 26 Jul 2021
Stein Variational Gradient Descent with Multiple Kernel Qingzhong Ai Shiyu Liu Lirong He Zenglin Xu 67 5 0 20 Jul 2021
Kernel Continual Learning Mohammad Mahdi Derakhshani Xiantong Zhen Ling Shao Cees G. M. Snoek CLL BDL VLM 120 38 0 12 Jul 2021
Meta-learning Amidst Heterogeneity and Ambiguity Kyeongryeol Go Seyoung Yun 82 1 0 05 Jul 2021
Gradient Importance Learning for Incomplete Observations Qitong Gao Dong Wang Joshua D. Amason Siyang Yuan Chenyang Tao Ricardo Henao M. Hadziahmetovic Lawrence Carin Miroslav Pajic 50 10 0 05 Jul 2021
Personalized Federated Learning with Gaussian Processes Idan Achituve Aviv Shamsian Aviv Navon Gal Chechik Ethan Fetaya FedML 87 103 0 29 Jun 2021
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models Lenart Treven Philippe Wenk Florian Dorfler Andreas Krause OOD 42 2 0 22 Jun 2021
Kernel Identification Through Transformers F. Simpson Ian Davies V. Lalchand A. Vullo N. Durrande C. Rasmussen 63 11 0 15 Jun 2021
Last Layer Marginal Likelihood for Invariance Learning Pola Schwobel Martin Jørgensen Sebastian W. Ober Mark van der Wilk BDL UQCV 100 30 0 14 Jun 2021
Probability Paths and the Structure of Predictions over Time Zhiyuan Jerry Lin Hao Sheng Sharad Goel 40 2 0 11 Jun 2021
Measuring the robustness of Gaussian processes to kernel choice William T. Stephenson S. Ghosh Tin D. Nguyen Mikhail Yurochkin Sameer K. Deshpande Tamara Broderick GP 35 11 0 11 Jun 2021
Learning Nonparametric Volterra Kernels with Gaussian Processes M. Ross M. Smith Mauricio A. Alvarez GP 31 7 0 10 Jun 2021
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data Jens Petersen Gregor Koehler David Zimmerer Fabian Isensee Paul F. Jäger Klaus H. Maier-Hein BDL AI4TS 71 3 0 09 Jun 2021
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning Antoine Grosnit Rasul Tutunov A. Maraval Ryan-Rhys Griffiths Alexander I. Cowen-Rivers ... Wenlong Lyu Zhitang Chen Jun Wang Jan Peters Haitham Bou-Ammar BDL DRL 97 61 0 07 Jun 2021
Meta-Learning Reliable Priors in the Function Space Jonas Rothfuss Dominique Heyn Jinfan Chen Andreas Krause 84 28 0 06 Jun 2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Jannik Kossen Neil Band Clare Lyle Aidan Gomez Tom Rainforth Y. Gal OOD 3DPC 120 142 0 04 Jun 2021
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning Zhiliang Wu Yinchong Yang Jindong Gu Volker Tresp UQCV MedIm 48 9 0 01 Jun 2021
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle J. Woo 108 11 0 30 May 2021
Understanding Uncertainty in Bayesian Deep Learning Cooper Lorsung BDL UQCV 21 0 0 21 May 2021
Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$ -Regularization Zheng Zhao Rui Gao Simo Särkkä 46 0 0 20 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 137 134 0 14 May 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 385 17 0 23 Apr 2021
Multivariate Deep Evidential Regression N. Meinert Alexander Lavin BDL PER EDL UQCV 85 21 0 13 Apr 2021
Uncertainty-aware Remaining Useful Life predictor Luca Biggio Alexander Wieland M. A. Chao I. Kastanis Olga Fink AI4CE 32 7 0 08 Apr 2021
Deep Gaussian Processes for Few-Shot Segmentation Joakim Johnander Johan Edstedt Martin Danelljan Michael Felsberg Fahad Shahbaz Khan VLM 69 2 0 30 Mar 2021
A Temporal Kernel Approach for Deep Learning with Continuous-time Information Da Xu Chuanwei Ruan Evren Körpeoglu Sushant Kumar Kannan Achan SyDa AI4TS 52 5 0 28 Mar 2021
Solving and Learning Nonlinear PDEs with Gaussian Processes Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 93 157 0 24 Mar 2021
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors Fan Shi Bin Li Xiangyang Xue LRM 70 9 0 22 Mar 2021
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey Kai Chen Qinglei Kong Yijue Dai Yue Xu Feng Yin Lexi Xu Shuguang Cui 102 32 0 18 Mar 2021
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models Zhen Lin Cao Xiao Lucas Glass M. P. M. Brandon Westover Jimeng Sun BDL 63 11 0 05 Mar 2021