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1301.3838
Cited By
Variational Relevance Vector Machines
Conference on Uncertainty in Artificial Intelligence (UAI), 2000
16 January 2013
Charles M. Bishop
Michael E. Tipping
DRL
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Papers citing
"Variational Relevance Vector Machines"
44 / 44 papers shown
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
Vincent C. Scholz
Yaohua Zang
P. Koutsourelakis
351
8
0
30 Jul 2024
Kernel Semi-Implicit Variational Inference
Ziheng Cheng
Longlin Yu
Tianyu Xie
Shiyue Zhang
Cheng Zhang
289
8
0
29 May 2024
Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Yuanhao Li
Badong Chen
N. Yoshimura
Yasuharu Koike
Okito Yamashita
304
4
0
01 Apr 2024
The Relevance Feature and Vector Machine for health applications
Albert Belenguer-Llorens
C. Sevilla-Salcedo
Emilio Parrado-Hernández
Vanessa Gómez-Verdejo
127
0
0
11 Feb 2024
Self-supervised Heterogeneous Graph Variational Autoencoders
Knowledge Discovery and Data Mining (KDD), 2023
Yige Zhao
Jianxiang Yu
Yao Cheng
Chengcheng Yu
Yiding Liu
Xiang Li
Shuaiqiang Wang
BDL
122
0
0
14 Nov 2023
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
Neural Information Processing Systems (NeurIPS), 2023
Longlin Yu
Tianyu Xie
Yu Zhu
Tong Yang
Xiangyu Zhang
Cheng Zhang
DiffM
322
14
0
26 Oct 2023
Canonical normalizing flows for manifold learning
Neural Information Processing Systems (NeurIPS), 2023
Kyriakos Flouris
E. Konukoglu
DRL
618
16
0
19 Oct 2023
Semi-Implicit Variational Inference via Score Matching
International Conference on Learning Representations (ICLR), 2023
Longlin Yu
Chuxu Zhang
424
20
0
19 Aug 2023
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination
IEEE International Joint Conference on Neural Network (IJCNN), 2023
Yuanhao Li
Badong Chen
Okito Yamashita
N. Yoshimura
Yasuharu Koike
244
4
0
31 Jan 2023
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
Neural Information Processing Systems (NeurIPS), 2022
Luning Sun
Daniel Zhengyu Huang
Hao Sun
Jian-Xun Wang
262
19
0
14 Oct 2022
RENs: Relevance Encoding Networks
Krithika S. Iyer
Riddhish Bhalodia
Shireen Y. Elhabian
DRL
300
1
0
25 May 2022
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022
Alexander Lin
Andrew H. Song
B. Bilgiç
Demba E. Ba
168
2
0
25 Feb 2022
Posterior Consistency for Bayesian Relevance Vector Machines
Journal of machine learning research (JMLR), 2022
X. Fang
M. Ghosh
BDL
159
0
0
11 Feb 2022
Analyzing Relevance Vector Machines using a single penalty approach
A. Dixit
Vivekananda Roy
209
0
0
05 Jul 2021
Covariance-Free Sparse Bayesian Learning
IEEE Transactions on Signal Processing (IEEE TSP), 2021
Alexander Lin
Andrew H. Song
B. Bilgiç
Demba E. Ba
207
36
0
21 May 2021
Sparse online variational Bayesian regression
K. Law
Vitaly Zankin
238
7
0
24 Feb 2021
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Longyuan Li
Junchi Yan
Xiaokang Yang
Yaohui Jin
OOD
BDL
AI4TS
219
69
0
31 Jan 2021
Posterior Impropriety of some Sparse Bayesian Learning Models
Statistics and Probability Letters (Stat. Probab. Lett.), 2020
A. Dixit
Vivekananda Roy
70
4
0
01 Aug 2020
Fully Bayesian Analysis of the Relevance Vector Machine Classification for Imbalanced Data
Wenyang Wang
Dongchu Sun
Zhuoqiong He
48
1
0
26 Jul 2020
Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion
Kazuaki Murayama
Shuichi Kawano
128
0
0
07 May 2020
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Journal of Computational Physics (JCP), 2019
Sebastian Kaltenbach
P. Koutsourelakis
AI4CE
573
40
0
30 Dec 2019
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
Panagiotis Tsilifis
I. Papaioannou
D. Štraub
F. Nobile
382
21
0
23 Dec 2019
Bayesian stochastic multi-scale analysis via energy considerations
Advanced Modeling and Simulation in Engineering Sciences (AMSES), 2019
M. Sarfaraz
B. Rosic
H. Matthies
A. Ibrahimbegovic
118
7
0
06 Dec 2019
A Sparse Bayesian Deep Learning Approach for Identification of Cascaded Tanks Benchmark
Hongpeng Zhou
Chahine Ibrahim
Wei Pan
131
6
0
15 Nov 2019
Thompson Sampling via Local Uncertainty
International Conference on Machine Learning (ICML), 2019
Zhendong Wang
Mingyuan Zhou
222
21
0
30 Oct 2019
Semi-Implicit Graph Variational Auto-Encoders
Neural Information Processing Systems (NeurIPS), 2019
Arman Hasanzadeh
Ehsan Hajiramezanali
N. Duffield
Krishna R. Narayanan
Mingyuan Zhou
Xiaoning Qian
BDL
GNN
293
151
0
19 Aug 2019
Asymptotically exact data augmentation: models, properties and algorithms
Maxime Vono
N. Dobigeon
P. Chainais
297
31
0
15 Feb 2019
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
Journal of Computational Physics (JCP), 2019
Constantin Grigo
P. Koutsourelakis
AI4CE
363
31
0
11 Feb 2019
A data-driven model order reduction approach for Stokes flow through random porous media
Constantin Grigo
P. Koutsourelakis
DiffM
AI4CE
50
0
0
21 Jun 2018
Semi-Implicit Variational Inference
Mingzhang Yin
Mingyuan Zhou
BDL
358
147
0
28 May 2018
Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography
L. Bruder
P. Koutsourelakis
MedIm
AI4CE
288
11
0
02 Mar 2018
Bayesian model and dimension reduction for uncertainty propagation: applications in random media
Constantin Grigo
P. Koutsourelakis
188
32
0
07 Nov 2017
Asynchronous Distributed Variational Gaussian Processes for Regression
Hao Peng
Shandian Zhe
Y. Qi
186
30
0
22 Apr 2017
Probabilistic Feature Selection and Classification Vector Machine
Bingbing Jiang
Chang Li
Maarten de Rijke
Xin Yao
Huanhuan Chen
252
44
0
18 Sep 2016
Predictive Coarse-Graining
M. Schöberl
N. Zabaras
P. Koutsourelakis
413
35
0
26 May 2016
Automatic Relevance Determination For Deep Generative Models
Theofanis Karaletsos
Gunnar Rätsch
269
8
0
28 May 2015
Non-parametric Bayesian Models of Response Function in Dynamic Image Sequences
O. Tichý
Václav Smídl
MedIm
72
7
0
19 Mar 2015
Hierarchical sparsity priors for regression models
Jim Griffin
P. Brown
325
2
0
19 Jul 2013
Mean field variational Bayesian inference for support vector machine classification
Computational Statistics & Data Analysis (CSDA), 2013
J. Luts
J. Ormerod
BDL
136
22
0
13 May 2013
A comparison of SVM and RVM for Document Classification
Muhammad Rafi
M. S. Shaikh
VLM
74
22
0
13 Jan 2013
Lognormal and Gamma Mixed Negative Binomial Regression
International Conference on Machine Learning (ICML), 2012
Mingyuan Zhou
Lingbo Li
David B. Dunson
Lawrence Carin
245
113
0
27 Jun 2012
Sparse Estimation using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
Niels Lovmand Pedersen
Carles Navarro i Manchon
Mihai-Alin Badiu
D. Shutin
B. Fleury
413
65
0
22 Aug 2011
Generalized Beta Mixtures of Gaussians
Neural Information Processing Systems (NeurIPS), 2011
Artin Armagan
David B. Dunson
M. Clyde
249
163
0
25 Jul 2011
Posterior consistency in linear models under shrinkage priors
Artin Armagan
David B. Dunson
Jaeyong Lee
W. Bajwa
Nate Strawn
510
34
0
20 Apr 2011
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