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1Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
S. Lan, S. Li, and B. Shahbaba. (2021)cite arxiv:2101.03906.
4 years ago by @peter.ralph
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ABC
calibration_emulation_sampling
Bayesian
inverse_problems
neural_networks
ABCcalibration_emulation_samplingBayesianinverse_problemsneural_networks
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2A Triangular Network For Density Estimation
X. Li. (2020)cite arxiv:2004.14593Comment: Universal density estimator. Supplements like code at https://github.com/lixilinx/TriNet4PdfEst.
6 years ago by @kirk86
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estimation
bayesian
autoregressive
density
estimationbayesianautoregressivedensity
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2An approximate KLD based experimental design for models with intractable likelihoods
Z. Ao, and J. Li. (2020)cite arxiv:2004.00715Comment: To appear in AISTATS 2020.
6 years ago by @kirk86
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approximate
bayesian
information
divergences
uncertainty
approximatebayesianinformationdivergencesuncertainty
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2Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
R. Zhang, C. Li, J. Zhang, C. Chen, and A. Wilson. International Conference on Learning Representations, (2020)
6 years ago by @kirk86
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bayesian
iclr2020
sampling
mcmc
bayesianiclr2020samplingmcmc
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1Learning deep kernels for exponential family densities
L. Wenliang, D. Sutherland, H. Strathmann, and A. Gretton. (2018)cite arxiv:1811.08357.
6 years ago by @kirk86
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bayesian
readings
stats
probability
kernels
bayesianreadingsstatsprobabilitykernels
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1'In-Between' Uncertainty in Bayesian Neural Networks
A. Foong, Y. Li, J. Hernández-Lobato, and R. Turner. (2019)cite arxiv:1906.11537Comment: Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning.
6 years ago by @kirk86
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bayesian
readings
uncertainty
bayesianreadingsuncertainty
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2Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
J. Li, Y. Zhou, and J. Zhu. International Conference on Learning Representations, (2020)
6 years ago by @kirk86
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approximate
bayesian
iclr2020
sampling
information
approximatebayesianiclr2020samplinginformation
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1A tutorial on Dirichlet process mixture modeling
Y. Li, E. Schofield, and M. Gönen. Journal of Mathematical Psychology, (2019)
6 years ago by @kirk86
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bayesian
tutorials
probability
bayesiantutorialsprobability
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2Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
X. Li, S. Chen, X. Hu, and J. Yang. (2018)cite arxiv:1801.05134Comment: 9 pages, 7 figures.
6 years ago by @kirk86
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bayesian
readings
regularisation
robustness
uncertainty
bayesianreadingsregularisationrobustnessuncertainty
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1Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks
A. Foong, D. Burt, Y. Li, and R. Turner. (2019)cite arxiv:1909.00719.
6 years ago by @kirk86
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bayesian
generative-models
uncertainty
bayesiangenerative-modelsuncertainty
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2Diverse Image Synthesis from Semantic Layouts via Conditional IMLE
K. Li, T. Zhang, and J. Malik. (2018)cite arxiv:1811.12373Comment: 18 pages, 16 figures; IEEE International Conference on Computer Vision (ICCV), 2019.
6 years ago by @kirk86
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bayesian
deep-learning
generative-models
bayesiandeep-learninggenerative-models
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2Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
B. Wang, J. Lu, Z. Yan, H. Luo, T. Li, Y. Zheng, and G. Zhang. (2018)cite arxiv:1812.09467.
7 years ago by @patrickz
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bayesian
deep-learning
uncertainty
bayesiandeep-learninguncertainty
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2Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
B. Wang, J. Lu, Z. Yan, H. Luo, T. Li, Y. Zheng, and G. Zhang. (2018)cite arxiv:1812.09467.
7 years ago by @kirk86
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bayesian
deep-learning
uncertainty
bayesiandeep-learninguncertainty
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1Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
R. Zhang, C. Li, J. Zhang, C. Chen, and A. Wilson. (2019)cite arxiv:1902.03932.
7 years ago by @jpvaldes
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bayesian
machine_learning
cyclical_stochastic_gradient
deep_learning
bayesianmachine_learningcyclical_stochastic_gradientdeep_learning
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1The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
B. Carpenter, M. Hoffman, M. Brubaker, D. Lee, P. Li, and M. Betancourt. (2015)cite arxiv:1509.07164Comment: 96 pages, 9 figures.
7 years ago by @jpvaldes
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bayesian
mcmc
stan
bayesianmcmcstan
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1Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization
T. Yang, Q. Lin, and Z. Li. (2016)cite arxiv:1604.03257v1.pdf.
10 years ago by @pixor
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bayesian
optimization
deeplearning
bayesianoptimizationdeeplearning
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2A Bayesian Hierarchical Model for Learning Natural Scene Categories.
F. Li, and P. Perona. CVPR (2), page 524-531. IEEE Computer Society, (2005)
12 years ago by @marcelkiesel
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bayesian
natural
hierarchical
learning
model
bayesiannaturalhierarchicallearningmodel
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1A class of discrete multiresolution random fields and its application to image segmentation
R. Wilson, and C. Li. IEEE Trans. on Pattern Analysis and Machine Intelligence, 25 (1): 42--56 (2003)
12 years ago by @guillem.palou
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image
segmentation,
level
Bayesian
methods
images,
model,
random
estimation
gray
images,Bayes
algorithms,
array,
representation,
textured
field
estimation,
multiresolution
segmentation
imagesegmentation,levelBayesianmethodsimages,model,randomestimationgrayimages,Bayesalgorithms,array,representation,texturedfieldestimation,multiresolutionsegmentation
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4Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
L. Fei-Fei, R. Fergus, and P. Perona. Computer Vision and Image Understanding, 106 (1): 59--70 (April 2007)
16 years ago by @mozaher
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Incremental
recognition,
Bayesian
learning,
model,
Object
model
Categorization,
Generative
Incrementalrecognition,Bayesianlearning,model,ObjectmodelCategorization,Generative
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1Bayesian method for learning graphical models with incompletely categorical data
Z. Geng, Y. He, X. Wang, and Q. Zhao. Computational Statistics & Data Analysis, 44 (1-2): 175--192 (Oct 28, 2003)
18 years ago by @smicha
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Bayesian
learning
Bayesianlearning
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publications (hide)20
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