Interacting Contour Stochastic Gradient Langevin Dynamics Article Swipe
Wei Deng
,
Siqi Liang
,
Botao Hao
,
Guang Lin
,
Faming Liang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.09867
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.09867
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equivalent computational budget. We also present a novel random-field function, which facilitates the estimation of self-adapting parameters in big data and obtains free mode explorations. Empirically, we compare the proposed algorithm with popular benchmark methods for posterior sampling. The numerical results show a great potential of ICSGLD for large-scale uncertainty estimation tasks.
Related Topics
Concepts
Langevin dynamics
Statistical physics
Computer science
Embarrassingly parallel
Benchmark (surveying)
Sampling (signal processing)
Function (biology)
Brownian dynamics
Langevin equation
Algorithm
Mathematics
Physics
Brownian motion
Statistics
Filter (signal processing)
Geography
Geodesy
Computer vision
Parallel algorithm
Biology
Evolutionary biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2202.09867
- https://arxiv.org/pdf/2202.09867
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221152049
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221152049Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2202.09867Digital Object Identifier
- Title
-
Interacting Contour Stochastic Gradient Langevin DynamicsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-20Full publication date if available
- Authors
-
Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming LiangList of authors in order
- Landing page
-
https://arxiv.org/abs/2202.09867Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2202.09867Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2202.09867Direct OA link when available
- Concepts
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Langevin dynamics, Statistical physics, Computer science, Embarrassingly parallel, Benchmark (surveying), Sampling (signal processing), Function (biology), Brownian dynamics, Langevin equation, Algorithm, Mathematics, Physics, Brownian motion, Statistics, Filter (signal processing), Geography, Geodesy, Computer vision, Parallel algorithm, Biology, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 3, 2022: 1, 2020: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.facilitates | 51 |
| abstract_inverted_index.interacting | 3 |
| abstract_inverted_index.large-scale | 88 |
| abstract_inverted_index.uncertainty | 89 |
| abstract_inverted_index.Empirically, | 65 |
| abstract_inverted_index.random-field | 48 |
| abstract_inverted_index.single-chain | 36 |
| abstract_inverted_index.computational | 41 |
| abstract_inverted_index.explorations. | 64 |
| abstract_inverted_index.interactions. | 24 |
| abstract_inverted_index.self-adapting | 55 |
| abstract_inverted_index.theoretically | 31 |
| abstract_inverted_index.embarrassingly | 12 |
| abstract_inverted_index.multiple-chain | 14 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |