Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 141 papers shown

Title
Integrating Random Effects in Deep Neural Networks Giora Simchoni Saharon Rosset BDL AI4CE 14 21 0 07 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 17 6 0 30 May 2022
Split personalities in Bayesian Neural Networks: the case for full marginalisation David Yallup Will Handley Michael P. Hobson A. Lasenby Pablo Lemos 22 1 0 23 May 2022
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation Alec M. Dunton Benjamin W. Priest Amanda Muyskens GP 24 3 0 22 May 2022
The Unreasonable Effectiveness of Deep Evidential Regression N. Meinert J. Gawlikowski Alexander Lavin UQCV EDL 175 35 0 20 May 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 30 4 0 15 May 2022
Efficient Learning of Inverse Dynamics Models for Adaptive Computed Torque Control David Jorge Gabriella Pizzuto M. Mistry 11 3 0 10 May 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 16 48 0 01 May 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks Zhijie Deng Jiaxin Shi Jun Zhu 16 18 0 30 Apr 2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders Samuel Stanton Wesley J. Maddox Nate Gruver Phillip M. Maffettone E. Delaney Peyton Greenside A. Wilson BDL 31 89 0 23 Mar 2022
E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction Shihong Wang Xueying Zhang Yichen Meng W. Xing 15 1 0 01 Mar 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 52 56 0 23 Feb 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 31 44 0 22 Feb 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 21 3 0 30 Jan 2022
Local Latent Space Bayesian Optimization over Structured Inputs N. Maus Haydn Jones Juston Moore Matt J. Kusner John Bradshaw J. Gardner BDL 49 69 0 28 Jan 2022
A Kernel-Expanded Stochastic Neural Network Y. Sun F. Liang 20 5 0 14 Jan 2022
Neural Fields as Learnable Kernels for 3D Reconstruction Francis Williams Zan Gojcic S. Khamis Denis Zorin Joan Bruna Sanja Fidler Or Litany 44 66 0 26 Nov 2021
Multi-Task Neural Processes Jiayi Shen Xiantong Zhen M. Worring Ling Shao BDL 16 8 0 10 Nov 2021
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes Hugh Dance Brooks Paige GP 20 10 0 08 Nov 2021
Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan Yalin Wang 11 2 0 30 Oct 2021
Improving Hyperparameter Optimization by Planning Ahead H. Jomaa Jonas K. Falkner Lars Schmidt-Thieme 16 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 16 9 0 14 Oct 2021
Dense Gaussian Processes for Few-Shot Segmentation Joakim Johnander Johan Edstedt M. Felsberg F. Khan Martin Danelljan 59 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 31 11 0 06 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 46 21 0 20 Sep 2021
BoA-PTA, A Bayesian Optimization Accelerated Error-Free SPICE Solver W. Xing X. Jin Yi Liu Dan Niu Weisheng Zhao Zhou Jin 11 4 0 31 Jul 2021
Stein Variational Gradient Descent with Multiple Kernel Qingzhong Ai Shiyu Liu Lirong He Zenglin Xu 17 4 0 20 Jul 2021
Meta-learning Amidst Heterogeneity and Ambiguity Kyeongryeol Go Seyoung Yun 24 1 0 05 Jul 2021
Personalized Federated Learning with Gaussian Processes Idan Achituve Aviv Shamsian Aviv Navon Gal Chechik Ethan Fetaya FedML 21 98 0 29 Jun 2021
Meta-Learning Reliable Priors in the Function Space Jonas Rothfuss Dominique Heyn Jinfan Chen Andreas Krause 26 27 0 06 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 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 63 17 0 23 Apr 2021
Multivariate Deep Evidential Regression N. Meinert Alexander Lavin BDL PER EDL UQCV 17 21 0 13 Apr 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 23 30 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 21 11 0 05 Mar 2021
Fast Adaptation with Linearized Neural Networks Wesley J. Maddox Shuai Tang Pablo G. Moreno A. Wilson Andreas C. Damianou 19 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 202 81 0 16 Feb 2021
Few-Shot Bayesian Optimization with Deep Kernel Surrogates Martin Wistuba Josif Grabocka BDL 29 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
Are wider nets better given the same number of parameters? A. Golubeva Behnam Neyshabur Guy Gur-Ari 16 44 0 27 Oct 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 6 25 0 22 Oct 2020
Few-shot Learning for Spatial Regression Tomoharu Iwata Yusuke Tanaka 9 11 0 09 Oct 2020
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 32 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 25 4 0 21 Jun 2020
NP-PROV: Neural Processes with Position-Relevant-Only Variances Xuesong Wang Lina Yao Xianzhi Wang Feiping Nie BDL 17 3 0 15 Jun 2020
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation Yifan Chen H. Owhadi Andrew M. Stuart 17 31 0 22 May 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 31 277 0 24 Feb 2020