Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Fast emulation of density functional theory simulations using approximate Gaussian processes S. Stetzler M. Grosskopf E. Lawrence 36 0 0 24 Aug 2022
Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves Max Lamparth Ludwig M. Böss U. Steinwandel K. Dolag 24 0 0 14 Aug 2022
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation Zhitong Xiong Haopeng Li Xiao Xiang Zhu 93 36 0 30 Jul 2022
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors Gianluca Detommaso Alberto Gasparin A. Wilson Cédric Archambeau UQCV BDL 78 3 0 17 Jul 2022
NP-Match: When Neural Processes meet Semi-Supervised Learning Jianfeng Wang Thomas Lukasiewicz Daniela Massiceti Xiaolin Hu Vladimir Pavlovic A. Neophytou BDL 140 41 0 03 Jul 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 100 0 0 27 Jun 2022
Shallow and Deep Nonparametric Convolutions for Gaussian Processes Thomas M. McDonald M. Ross M. Smith Mauricio A. Alvarez 60 1 0 17 Jun 2022
Zero-Shot AutoML with Pretrained Models Ekrem Öztürk Fabio Ferreira H. Jomaa Lars Schmidt-Thieme Josif Grabocka Frank Hutter VLM 112 10 0 16 Jun 2022
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation Sebastian Ament Carla P. Gomes 62 9 0 16 Jun 2022
Deep Variational Implicit Processes Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL 63 5 0 14 Jun 2022
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process Hao Yu Kaiyang Guo Mahdi Karami Xi Chen Guojun Zhang Pascal Poupart FedML 89 3 0 13 Jun 2022
Multi-fidelity Hierarchical Neural Processes D. Wu Matteo Chinazzi Alessandro Vespignani Yi-An Ma Rose Yu AI4CE 62 13 0 10 Jun 2022
Evaluating Aleatoric Uncertainty via Conditional Generative Models Ziyi Huang Henry Lam Haofeng Zhang PER UD 59 5 0 09 Jun 2022
Neural Diffusion Processes Vincent Dutordoir Alan D. Saul Zoubin Ghahramani F. Simpson DiffM 112 42 0 08 Jun 2022
Integrating Random Effects in Deep Neural Networks Giora Simchoni Saharon Rosset BDL AI4CE 129 21 0 07 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 71 6 0 30 May 2022
Semi-Parametric Inducing Point Networks and Neural Processes R. Rastogi Yair Schiff Alon Hacohen Zhaozhi Li I-Hsiang Lee Yuntian Deng M. Sabuncu Volodymyr Kuleshov 3DPC 82 7 0 24 May 2022
Split personalities in Bayesian Neural Networks: the case for full marginalisation David Yallup Will Handley Michael P. Hobson A. Lasenby Pablo Lemos 49 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 60 3 0 22 May 2022
The Unreasonable Effectiveness of Deep Evidential Regression N. Meinert J. Gawlikowski Alexander Lavin UQCV EDL 275 37 0 20 May 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 73 4 0 15 May 2022
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification Che-Chia Chang T. Zeng GP 50 6 0 13 May 2022
AK: Attentive Kernel for Information Gathering Weizhe (Wesley) Chen Roni Khardon Lantao Liu 120 13 0 13 May 2022
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning Veit Wild Robert Hu Dino Sejdinovic BDL 135 13 0 12 May 2022
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models Kabir Ahuja Shanu Kumar Sandipan Dandapat Monojit Choudhury 57 25 0 12 May 2022
Efficient Learning of Inverse Dynamics Models for Adaptive Computed Torque Control David Jorge Gabriella Pizzuto M. Mistry 41 3 0 10 May 2022
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Jiajun He Austin Tripp José Miguel Hernández-Lobato 66 23 0 05 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 231 51 0 01 May 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks Zhijie Deng Jiaxin Shi Jun Zhu 134 19 0 30 Apr 2022
Deep Ensemble as a Gaussian Process Approximate Posterior Zhijie Deng Feng Zhou Jianfei Chen Guoqiang Wu Jun Zhu UQCV 41 5 0 30 Apr 2022
Inducing Gaussian Process Networks Alessandro Tibo Thomas D. Nielsen BDL 21 1 0 21 Apr 2022
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees Wenying Deng Beau Coker Rajarshi Mukherjee J. Liu B. Coull 63 2 0 15 Apr 2022
LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections Dario Sitnik I. Kopriva MedIm 70 3 0 14 Apr 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor Wesley J. Maddox Pavel Izmailov A. Wilson BDL UD 94 51 0 30 Mar 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 77 98 0 23 Mar 2022
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed Toni Karvonen Chris J. Oates GP 78 26 0 17 Mar 2022
Learning Representation for Bayesian Optimization with Collision-free Regularization Fengxue Zhang Brian D. Nord Yuxin Chen OOD BDL 42 2 0 16 Mar 2022
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel Chi-Ken Lu Patrick Shafto BDL 76 0 0 14 Mar 2022
Bayesian Calibration for Activity Based Models Laura Schultz Joshua A. Auld Vadim Sokolov 63 3 0 08 Mar 2022
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes Felix Jimenez Matthias Katzfuss 69 10 0 02 Mar 2022
E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction Shihong Wang Xueying Zhang Yichen Meng W. Xing 49 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 149 58 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 147 48 0 22 Feb 2022
Supervising the Multi-Fidelity Race of Hyperparameter Configurations Martin Wistuba Arlind Kadra Josif Grabocka 100 15 0 20 Feb 2022
Augmenting Neural Networks with Priors on Function Values Hunter Nisonoff Yixin Wang Jennifer Listgarten 74 3 0 10 Feb 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 93 3 0 30 Jan 2022
Local Latent Space Bayesian Optimization over Structured Inputs Natalie Maus Haydn Thomas Jones Juston Moore Matt J. Kusner John Bradshaw Jacob R. Gardner BDL 118 72 0 28 Jan 2022
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models Xiaofan Zhang Zongwei Zhou Deming Chen Yu Emma Wang 81 11 0 21 Jan 2022
A Kernel-Expanded Stochastic Neural Network Y. Sun F. Liang 58 7 0 14 Jan 2022
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations Srinivas Anumasa P. K. Srijith OOD UQCV BDL 76 11 0 23 Dec 2021