Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Deep Convolutional Networks as shallow Gaussian Processes Adrià Garriga-Alonso C. Rasmussen Laurence Aitchison BDL UQCV 116 271 0 16 Aug 2018
Kernel Flows: from learning kernels from data into the abyss H. Owhadi G. Yoo 110 90 0 13 Aug 2018
The Deep Kernelized Autoencoder Michael C. Kampffmeyer Sigurd Løkse F. Bianchi Robert Jenssen L. Livi 64 18 0 19 Jul 2018
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) Trefor W. Evans P. Nair GP 52 25 0 05 Jul 2018
Neural Processes M. Garnelo Jonathan Richard Schwarz Dan Rosenbaum Fabio Viola Danilo Jimenez Rezende S. M. Ali Eslami Yee Whye Teh BDL UQCV GP 107 517 0 04 Jul 2018
Conditional Neural Processes M. Garnelo Dan Rosenbaum Chris J. Maddison Tiago Ramalho D. Saxton Murray Shanahan Yee Whye Teh Danilo Jimenez Rezende S. M. Ali Eslami UQCV BDL 94 707 0 04 Jul 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 144 697 0 03 Jul 2018
Stagewise Safe Bayesian Optimization with Gaussian Processes Yanan Sui Vincent Zhuang J. W. Burdick Yisong Yue 111 143 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 83 70 0 12 Jun 2018
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty Hao Zhou Yunyang Xiong Vikas Singh UQCV BDL 85 4 0 10 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 199 1,008 0 05 Jun 2018
Deep Gaussian Processes with Convolutional Kernels Vinayak Kumar Vaibhav Singh P. K. Srijith Andreas C. Damianou BDL GP 101 30 0 05 Jun 2018
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare Ingyo Chung Saehoon Kim Juho Lee Kwang Joon Kim Sung Ju Hwang Eunho Yang BDL FedML 82 16 0 05 Jun 2018
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression Haitao Liu Jianfei Cai Yi Wang Yew-Soon Ong 81 84 0 03 Jun 2018
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance Neal Jean Sang Michael Xie Stefano Ermon BDL SSL 79 77 0 26 May 2018
Multi-layer Kernel Ridge Regression for One-class Classification C. Gautam Aruna Tiwari Sundaram Suresh Alexandros Iosifidis 22 3 0 20 May 2018
Accurate Kernel Learning for Linear Gaussian Markov Processes using a Scalable Likelihood Computation S. Waele 25 1 0 18 May 2018
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots Akshara Rai Rika Antonova Franziska Meier C. Atkeson 89 29 0 07 May 2018
Gaussian Process Behaviour in Wide Deep Neural Networks A. G. Matthews Mark Rowland Jiri Hron Richard Turner Zoubin Ghahramani BDL 195 561 0 30 Apr 2018
Deep Embedding Kernel Linh Le Ying Xie 67 10 0 16 Apr 2018
Learning through deterministic assignment of hidden parameters Jian Fang Shaobo Lin Zongben Xu 30 7 0 22 Mar 2018
Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss Jacob R. Gardner Kilian Q. Weinberger A. Wilson 80 96 0 16 Mar 2018
Malytics: A Malware Detection Scheme Mahmood Yousefi-Azar Len Hamey Vijay Varadharajan Shiping Chen 61 40 0 09 Mar 2018
On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation Shiyu Duan Shujian Yu Yunmei Chen José C. Príncipe 36 16 0 11 Feb 2018
Algorithmic Linearly Constrained Gaussian Processes Markus Lange-Hegermann 87 35 0 28 Jan 2018
Stacked Kernel Network Shuai Zhang Jianxin Li P. Xie Yingchun Zhang Minglai Shao Haoyi Zhou Mengyi Yan 202 9 0 25 Nov 2017
Deep Expander Networks: Efficient Deep Networks from Graph Theory Ameya Prabhu G. Varma A. Namboodiri GNN 130 72 0 23 Nov 2017
Optimizing Kernel Machines using Deep Learning Huan Song Jayaraman J. Thiagarajan P. Sattigeri A. Spanias 75 53 0 15 Nov 2017
Scalable Log Determinants for Gaussian Process Kernel Learning Kun Dong David Eriksson H. Nickisch D. Bindel A. Wilson 69 95 0 09 Nov 2017
Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation Roman Föll B. Haasdonk Markus Hanselmann Holger Ulmer BDL 63 6 0 02 Nov 2017
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 160 1,100 0 01 Nov 2017
Auto-Differentiating Linear Algebra Matthias Seeger A. Hetzel Zhenwen Dai Eric Meissner Neil D. Lawrence 70 40 0 24 Oct 2017
Improving One-Shot Learning through Fusing Side Information Yao-Hung Hubert Tsai Ruslan Salakhutdinov FedML 53 51 0 23 Oct 2017
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition Pavel Izmailov Alexander Novikov D. Kropotov 93 62 0 19 Oct 2017
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes J. Liu B. Coull 48 10 0 03 Oct 2017
A representer theorem for deep kernel learning B. Bohn M. Griebel C. Rieger 78 56 0 29 Sep 2017
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes Tomoharu Iwata Zoubin Ghahramani UQCV BDL 71 42 0 19 Jul 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 123 172 0 08 Jul 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 301 5,890 0 14 Jun 2017
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes Hyunjik Kim Yee Whye Teh 77 52 0 08 Jun 2017
Contextual Explanation Networks Maruan Al-Shedivat Kumar Avinava Dubey Eric Xing CML 101 83 0 29 May 2017
Bayesian GAN Yunus Saatci A. Wilson GAN 106 133 0 26 May 2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni M. Deisenroth BDL GP 115 424 0 24 May 2017
MMD GAN: Towards Deeper Understanding of Moment Matching Network Chun-Liang Li Wei-Cheng Chang Yu Cheng Yiming Yang Barnabás Póczós GAN 70 726 0 24 May 2017
The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables L. Ambrogioni Umut Güçlü Marcel van Gerven E. Maris BDL 195 47 0 19 May 2017
Multimodal Word Distributions Ben Athiwaratkun A. Wilson 104 90 0 27 Apr 2017
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning Prasoon Goyal Zhiting Hu Xiaodan Liang Chenyu Wang Eric Xing CML BDL 92 117 0 21 Mar 2017
Bayesian Optimization with Gradients Jian Wu Matthias Poloczek A. Wilson P. Frazier 80 210 0 13 Mar 2017
Deep Radial Kernel Networks: Approximating Radially Symmetric Functions with Deep Networks B. McCane Lech Szymanski 83 6 0 09 Mar 2017
Deep Kernelized Autoencoders Michael C. Kampffmeyer Sigurd Løkse F. Bianchi Robert Jenssen L. Livi 73 18 0 08 Feb 2017