Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression Anshuman Pradhan Kyra H Adams Venkat Chandrasekaran Zhen Liu J. Reager Andrew M. Stuart M. Turmon 36 0 0 23 Oct 2023
Thin and Deep Gaussian Processes Daniel Augusto R. M. A. de Souza Alexander Nikitin S. T. John Magnus Ross Mauricio A. Alvarez M. Deisenroth Joao P. P. Gomes Diego Mesquita C. L. C. Mattos 80 5 0 17 Oct 2023
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification Tianjun Ke Haoqun Cao Zenan Ling Feng Zhou UQCV 64 8 0 16 Oct 2023
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes Yixuan Zhang Quyu Kong Feng Zhou 44 4 0 09 Oct 2023
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels Fan He Ming-qian He Lei Shi Xiaolin Huang Johan A. K. Suykens 59 1 0 08 Oct 2023
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels Alexander Shmakov Avisek Naug Vineet Gundecha Sahand Ghorbanpour Ricardo Luna Gutierrez Ashwin Ramesh Babu Antonio Guillen-Perez Soumyendu Sarkar 101 11 0 05 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 103 20 0 28 Sep 2023
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head Alan Q. Wang Minh Nguyen M. Sabuncu CML OOD 89 1 0 23 Sep 2023
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes M. Noack Hengrui Luo M. Risser GP 104 13 0 18 Sep 2023
Convolutional Deep Kernel Machines Edward Milsom Ben Anson Laurence Aitchison BDL 96 5 0 18 Sep 2023
To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data Hechuan Wen Tong Chen Li Kheng Chai S. Sadiq Kai Zheng Hongzhi Yin CML 89 2 0 15 Sep 2023
Promises of Deep Kernel Learning for Control Synthesis Robert Reed Luca Laurenti Morteza Lahijanian BDL 52 5 0 12 Sep 2023
Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning Mohamadreza Jafaryani H. Sheikhzadeh V. Pourahmadi 76 4 0 08 Sep 2023
Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study A. Hazra Pratik Nag Rishikesh Yadav Ying Sun 61 4 0 10 Aug 2023
Kernelised Normalising Flows Eshant English Matthias Kirchler C. Lippert TPM 95 0 0 27 Jul 2023
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023 Yu. V. Gorishniy Ivan Rubachev Nikolay Kartashev Daniil Shlenskii Akim Kotelnikov Artem Babenko OOD LMTD 85 15 0 26 Jul 2023
A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia Diana McSpadden S. Goldenberg Bina Roy M. Schram J. Goodall H. Lipford AI4CE 55 4 0 26 Jul 2023
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation Fengxue Zhang Jialin Song James Bowden Alexander Ladd Yisong Yue Thomas Desautels Yuxin Chen 82 7 0 25 Jul 2023
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation S. Bouabid Dino Sejdinovic D. Watson‐Parris 61 5 0 14 Jul 2023
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space Zhengdao Chen 90 1 0 03 Jul 2023
Density Uncertainty Layers for Reliable Uncertainty Estimation Yookoon Park David M. Blei UQCV BDL 53 2 0 21 Jun 2023
Multi-Fidelity Active Learning with GFlowNets Alex Hernandez-Garcia Nikita Saxena Moksh Jain Cheng-Hao Liu Yoshua Bengio AI4CE 86 15 0 20 Jun 2023
Practical Equivariances via Relational Conditional Neural Processes Daolang Huang Manuel Haussmann Ulpu Remes S. T. John Grégoire Clarté K. Luck Samuel Kaski Luigi Acerbi BDL 150 9 0 19 Jun 2023
Collapsed Inference for Bayesian Deep Learning Zhe Zeng Guy Van den Broeck FedML BDL UQCV 126 9 0 16 Jun 2023
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes Weizhe (Wesley) Chen Roni Khardon Lantao Liu 96 10 0 02 Jun 2023
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization Navid Ansari Hans-Peter Seidel Vahid Babaei 65 2 0 01 Jun 2023
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization Y. Li Tim G. J. Rudner A. Wilson BDL 97 34 0 31 May 2023
Neural Kernel Surface Reconstruction Jiahui Huang Zan Gojcic Matan Atzmon Or Litany Sanja Fidler Francis Williams 3DV 102 74 0 31 May 2023
NicePIM: Design Space Exploration for Processing-In-Memory DNN Accelerators with 3D-Stacked-DRAM Junpeng Wang Mengke Ge Bo Ding Qi Xu Song Chen Yi Kang 47 6 0 30 May 2023
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks Felix Jimenez Matthias Katzfuss BDL UQCV 109 1 0 26 May 2023
The Representation Jensen-Shannon Divergence J. Hoyos-Osorio Santiago Posso-Murillo L. S. Giraldo 155 8 0 25 May 2023
Inverse Protein Folding Using Deep Bayesian Optimization Natalie Maus Yimeng Zeng Daniel A. Anderson Phillip M. Maffettone Aaron C. Solomon Peyton Greenside Osbert Bastani Jacob R. Gardner 95 3 0 25 May 2023
Deep Pipeline Embeddings for AutoML Sebastian Pineda Arango Josif Grabocka 112 2 0 23 May 2023
Physics Inspired Approaches To Understanding Gaussian Processes Maximilian P. Niroomand L. Dicks Edward O. Pyzer-Knapp D. Wales 72 1 0 18 May 2023
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs Pau Batlle Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 80 18 0 08 May 2023
Gaussian process deconvolution Felipe A. Tobar Arnaud Robert Jorge F. Silva 70 5 0 08 May 2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 116 81 0 07 May 2023
Hyperparameter Optimization through Neural Network Partitioning Bruno Mlodozeniec M. Reisser Christos Louizos 96 8 0 28 Apr 2023
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes Louis C. Tiao Vincent Dutordoir Victor Picheny BDL 64 0 0 27 Apr 2023
Experience-Based Evolutionary Algorithms for Expensive Optimization Xunzhao Yu Yan Wang Ling Zhu Dimitar Filev Xin Yao 66 2 0 09 Apr 2023
Biological Sequence Kernels with Guaranteed Flexibility Alan N. Amin Eli N. Weinstein D. Marks 80 4 0 06 Apr 2023
Self-Distillation for Gaussian Process Regression and Classification Kenneth Borup L. Andersen 54 2 0 05 Apr 2023
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments Arpan Biswas Yongtao Liu Nicole Creange Yu-Chen Liu S. Jesse Jan-Chi Yang Sergei V. Kalinin M. Ziatdinov Rama K Vasudevan 93 5 0 05 Apr 2023
Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma Tom Rainforth Yee Whye Teh Vincent Fortuin SSL UQCV BDL 98 10 0 04 Apr 2023
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach Mohit Kumar Bernhard A. Moser Lukas Fischer 73 3 0 03 Apr 2023
Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes Yifan Chen H. Owhadi F. Schafer 96 31 0 03 Apr 2023
Deep Ranking Ensembles for Hyperparameter Optimization Abdus Salam Khazi Sebastian Pineda Arango Josif Grabocka BDL 85 7 0 27 Mar 2023
Deep Kernel Methods Learn Better: From Cards to Process Optimization Mani Valleti Rama K Vasudevan M. Ziatdinov Sergei V. Kalinin BDL 36 9 0 25 Mar 2023
Learning a Depth Covariance Function Eric Dexheimer Andrew J. Davison MDE 105 15 0 21 Mar 2023
Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification Firas Al-Hindawi M. R. Siddiquee Teresa Wu Hang-Rui Hu Ying Sun 54 5 0 18 Mar 2023