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Microstructure modeling and computational micromechanics of glued metallic hollow-sphere composites. [PDF]
Arch Appl MechJabs L +4 more
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IGANets: Isogeometric analysis networks and their applications to linear structural analysis problems. [PDF]
Eng ComputMöller M +5 more
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The Supervised Hierarchical Dirichlet Process [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model.
Amos Storkey
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Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process
IEEE International Conference on Computer Vision, 2021Recent research efforts in lifelong learning propose to grow a mixture of models to adapt to an increasing number of tasks. The proposed methodology shows promising results in overcoming catastrophic forgetting.
Fei Ye, A. Bors
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, 2021
With the expansion of modern plants and the growth of process complexities, industrial processes generally have multimodes, which are reflected in the multiple distributions and complex correlations among the process data.
Jie Dong, Chi Zhang, Kaixiang Peng
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With the expansion of modern plants and the growth of process complexities, industrial processes generally have multimodes, which are reflected in the multiple distributions and complex correlations among the process data.
Jie Dong, Chi Zhang, Kaixiang Peng
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Nested Hierarchical Dirichlet Processes [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Bayesian ...
Michael I. Jordan +2 more
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Statistics & Probability Letters, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
MULIERE, PIETRO, WALKER S.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
MULIERE, PIETRO, WALKER S.
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Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013
Dirichlet process mixture (DPM) model is one of the most important Bayesian nonparametric models owing to its efficiency of inference and flexibility for various applications. A fundamental assumption made by DPM model is that all data items are generated from a single, shared DP.
Lijing Qin, Xiaoyan Zhu 0001
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Dirichlet process mixture (DPM) model is one of the most important Bayesian nonparametric models owing to its efficiency of inference and flexibility for various applications. A fundamental assumption made by DPM model is that all data items are generated from a single, shared DP.
Lijing Qin, Xiaoyan Zhu 0001
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Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures
IEEE Transactions on Signal Processing, 2020Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a ...
Cao-Yuan Li +6 more
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