Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 174 papers shown

Title
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice Jonas Rothfuss Martin Josifoski Vincent Fortuin Andreas Krause 43 7 0 14 Nov 2022
Uncertainty Quantification for Atlas-Level Cell Type Transfer J. Engelmann Leon Hetzel Giovanni Palla L. Sikkema Malte D. Luecken Fabian J. Theis 27 3 0 07 Nov 2022
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach Ankush Chakrabarty 24 9 0 31 Oct 2022
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport M. Bitzer Mona Meister Christoph Zimmer 12 9 0 21 Oct 2022
Locally Smoothed Gaussian Process Regression Davit Gogolashvili B. Kozyrskiy Maurizio Filippone 30 8 0 18 Oct 2022
Compositional Law Parsing with Latent Random Functions Fan Shi Bin Li Xiangyang Xue CoGe 21 4 0 15 Sep 2022
The Neural Process Family: Survey, Applications and Perspectives Saurav Jha Dong Gong Xuesong Wang Richard Turner L. Yao BDL 76 24 0 01 Sep 2022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields Niklas Schmitz Klaus-Robert Muller Stefan Chmiela AI4CE 21 11 0 25 Aug 2022
Fast emulation of density functional theory simulations using approximate Gaussian processes S. Stetzler M. Grosskopf E. Lawrence 23 0 0 24 Aug 2022
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation Zhitong Xiong Haopeng Li Xiao Xiang Zhu 42 35 0 30 Jul 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 29 0 0 27 Jun 2022
Integrating Random Effects in Deep Neural Networks Giora Simchoni Saharon Rosset BDL AI4CE 35 21 0 07 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 19 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 25 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 32 3 0 22 May 2022
The Unreasonable Effectiveness of Deep Evidential Regression N. Meinert J. Gawlikowski Alexander Lavin UQCV EDL 177 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 26 48 0 01 May 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks Zhijie Deng Jiaxin Shi Jun Zhu 27 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 38 89 0 23 Mar 2022
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes Felix Jimenez Matthias Katzfuss 18 10 0 02 Mar 2022
E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction Shihong Wang Xueying Zhang Yichen Meng W. Xing 20 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 39 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 29 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 55 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 50 66 0 26 Nov 2021
Multi-Task Neural Processes Jiayi Shen Xiantong Zhen M. Worring Ling Shao BDL 21 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 18 2 0 30 Oct 2021
Improving Hyperparameter Optimization by Planning Ahead H. Jomaa Jonas K. Falkner Lars Schmidt-Thieme 22 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 18 9 0 14 Oct 2021
Dense Gaussian Processes for Few-Shot Segmentation Joakim Johnander Johan Edstedt M. Felsberg Fahad Shahbaz Khan Martin Danelljan 61 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 34 11 0 06 Oct 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 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 48 21 0 20 Sep 2021
Toward a `Standard Model' of Machine Learning Zhiting Hu Eric P. Xing 37 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 16 4 0 31 Jul 2021
Stein Variational Gradient Descent with Multiple Kernel Qingzhong Ai Shiyu Liu Lirong He Zenglin Xu 22 4 0 20 Jul 2021
Meta-learning Amidst Heterogeneity and Ambiguity Kyeongryeol Go Seyoung Yun 29 1 0 05 Jul 2021
Personalized Federated Learning with Gaussian Processes Idan Achituve Aviv Shamsian Aviv Navon Gal Chechik Ethan Fetaya FedML 32 99 0 29 Jun 2021
Meta-Learning Reliable Priors in the Function Space Jonas Rothfuss Dominique Heyn Jinfan Chen Andreas Krause 40 27 0 06 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 31 124 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 100 17 0 23 Apr 2021
Multivariate Deep Evidential Regression N. Meinert Alexander Lavin BDL PER EDL UQCV 35 21 0 13 Apr 2021
Deep Gaussian Processes for Few-Shot Segmentation Joakim Johnander Johan Edstedt Martin Danelljan M. Felsberg Fahad Shahbaz Khan VLM 17 2 0 30 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 35 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 29 11 0 05 Mar 2021