Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Efficient Transfer Bayesian Optimization with Auxiliary Information Tomoharu Iwata Takuma Otsuka 69 2 0 17 Sep 2019
Deep kernel learning for integral measurements Carl Jidling J. Hendriks Thomas B. Schon A. Wills 69 7 0 04 Sep 2019
Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel Yao-Hung Hubert Tsai Shaojie Bai M. Yamada Louis-Philippe Morency Ruslan Salakhutdinov 173 261 0 30 Aug 2019
Deep Kernel Learning for Clustering Chieh-Tsai Wu Zulqarnain Khan Yale Chang Stratis Ioannidis Jennifer Dy 158 13 0 09 Aug 2019
Bayesian Batch Active Learning as Sparse Subset Approximation Robert Pinsler Jonathan Gordon Eric T. Nalisnick José Miguel Hernández-Lobato UQCV 86 132 0 06 Aug 2019
A Fine-Grained Spectral Perspective on Neural Networks Greg Yang Hadi Salman 125 113 0 24 Jul 2019
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 97 144 0 17 Jul 2019
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression Rika Antonova Akshara Rai Tianyu Li Danica Kragic DRL 120 19 0 10 Jul 2019
GP-VAE: Deep Probabilistic Time Series Imputation Vincent Fortuin Dmitry Baranchuk Gunnar Rätsch Stephan Mandt BDL AI4TS 119 258 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 61 0 0 03 Jul 2019
The Functional Neural Process Christos Louizos Xiahan Shi Klamer Schutte Max Welling BDL 82 77 0 19 Jun 2019
Robust Regression for Safe Exploration in Control Anqi Liu Guanya Shi Soon-Jo Chung Anima Anandkumar Yisong Yue 92 60 0 13 Jun 2019
Coupled Variational Recurrent Collaborative Filtering Qingquan Song Shiyu Chang Helen Zhou BDL 41 9 0 11 Jun 2019
Deep Compositional Spatial Models A. Zammit‐Mangion T. L. J. Ng Quan Vu Maurizio Filippone 126 57 0 06 Jun 2019
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings Jean-François Ton Lucian Chan Yee Whye Teh Dino Sejdinovic 71 13 0 05 Jun 2019
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel Xin Qiu Elliot Meyerson Risto Miikkulainen UQCV 90 54 0 03 Jun 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 58 3 0 31 May 2019
Adaptive Deep Kernel Learning Prudencio Tossou Basile Dura François Laviolette M. Marchand Alexandre Lacoste 98 29 0 28 May 2019
Scalable Training of Inference Networks for Gaussian-Process Models Jiaxin Shi Mohammad Emtiyaz Khan Jun Zhu BDL 49 18 0 27 May 2019
Interpretable deep Gaussian processes with moments Chi-Ken Lu Scott Cheng-Hsin Yang Xiaoran Hao Patrick Shafto 84 19 0 27 May 2019
Learning spectrograms with convolutional spectral kernels Zheyan Shen Markus Heinonen Samuel Kaski 64 9 0 23 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 68 9 0 14 May 2019
A Survey on Neural Architecture Search Martin Wistuba Ambrish Rawat Tejaswini Pedapati AI4CE 100 259 0 04 May 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 283 928 0 26 Apr 2019
Meta-Learning surrogate models for sequential decision making Alexandre Galashov Jonathan Richard Schwarz Hyunjik Kim M. Garnelo D. Saxton Pushmeet Kohli S. M. Ali Eslami Yee Whye Teh BDL OffRL 95 25 0 28 Mar 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 64 231 0 19 Mar 2019
Implicit Kernel Learning Chun-Liang Li Wei-Cheng Chang Youssef Mroueh Yiming Yang Barnabás Póczós VLM 74 42 0 26 Feb 2019
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach Minyoung Kim Pritish Sahu Behnam Gholami Vladimir Pavlovic OOD 70 47 0 23 Feb 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang 204 289 0 13 Feb 2019
Learnable Embedding Space for Efficient Neural Architecture Compression Shengcao Cao Xiaofang Wang Kris Kitani 89 43 0 01 Feb 2019
Functional Regularisation for Continual Learning with Gaussian Processes Michalis K. Titsias Jonathan Richard Schwarz A. G. Matthews Razvan Pascanu Yee Whye Teh CLL BDL 73 187 0 31 Jan 2019
Meta-Learning Mean Functions for Gaussian Processes Vincent Fortuin Heiko Strathmann Gunnar Rätsch BDL FedML MLT 117 29 0 23 Jan 2019
Kernel Change-point Detection with Auxiliary Deep Generative Models Wei-Cheng Chang Chun-Liang Li Yiming Yang Barnabás Póczós 101 71 0 18 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 112 442 0 17 Jan 2019
Cascaded Coarse-to-Fine Deep Kernel Networks for Efficient Satellite Image Change Detection H. Sahbi 28 0 0 21 Dec 2018
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach C. Lataniotis S. Marelli Bruno Sudret 45 67 0 15 Dec 2018
Supervised Deep Kriging for Single-Image Super-Resolution Gianni Franchi Angela Yao A. Kolb SupR 37 5 0 10 Dec 2018
Neural Non-Stationary Spectral Kernel Sami Remes Markus Heinonen Samuel Kaski BDL 50 9 0 27 Nov 2018
Learning deep kernels for exponential family densities W. Li Danica J. Sutherland Heiko Strathmann Arthur Gretton BDL 103 74 0 20 Nov 2018
Understanding and Comparing Scalable Gaussian Process Regression for Big Data Haitao Liu Jianfei Cai Yew-Soon Ong Yi Wang 75 26 0 03 Nov 2018
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction William Herlands Daniel B. Neill H. Nickisch A. Wilson OOD 62 2 0 28 Oct 2018
Gaussian Process Prior Variational Autoencoders F. P. Casale Adrian Dalca Luca Saglietti Jennifer Listgarten Nicolò Fusi BDL CML 75 139 0 28 Oct 2018
Hyperparameter Learning via Distributional Transfer H. Law P. Zhao Lucian Chan Junzhou Huang Dino Sejdinovic 85 25 0 15 Oct 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 125 310 0 11 Oct 2018
Deep learning with differential Gaussian process flows Pashupati Hegde Markus Heinonen Harri Lähdesmäki Samuel Kaski BDL 90 42 0 09 Oct 2018
Deep convolutional Gaussian processes Kenneth Blomqvist Samuel Kaski Markus Heinonen BDL 91 61 0 06 Oct 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 151 1,106 0 28 Sep 2018
A kernel-based approach to molecular conformation analysis Stefan Klus A. Bittracher Ingmar Schuster Christof Schütte 55 26 0 28 Sep 2018
Discretely Relaxing Continuous Variables for tractable Variational Inference Trefor W. Evans P. Nair BDL 57 0 0 12 Sep 2018
Learning Data-adaptive Nonparametric Kernels Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li 83 18 0 31 Aug 2018