Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC Article Swipe
Erik Nijkamp
,
Ruiqi Gao
,
Pavel Sountsov
,
Vasudevan Srinivas
,
Bo Pang
,
Song‐Chun Zhu
,
Ying Wu
·
YOU?
·
· 2020
· Open Access
·
YOU?
·
· 2020
· Open Access
·
Learning energy-based model (EBM) requires MCMC sampling of the learned model as the inner loop of the learning algorithm. However, MCMC sampling of EBM in data space is generally not mixing, because the energy function, which is usually parametrized by deep network, is highly multi-modal in the data space. This is a serious handicap for both the theory and practice of EBM. In this paper, we propose to learn EBM with a flow-based model serving as a backbone, so that the EBM is a correction or an exponential tilting of the flow-based model. We show that the model has a particularly simple form in the space of the latent variables of the flow-based model, and MCMC sampling of the EBM in the latent space, which is a simple special case of neural transport MCMC, mixes well and traverses modes in the data space. This enables proper sampling and learning of EBM.
Related Topics
Concepts
Markov chain Monte Carlo
Computer science
Sampling (signal processing)
Space (punctuation)
Simple (philosophy)
Flow (mathematics)
Artificial neural network
Artificial intelligence
Latent variable
Energy (signal processing)
Machine learning
Mathematics
Statistics
Bayesian probability
Computer vision
Filter (signal processing)
Geometry
Epistemology
Operating system
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/2006.06897.pdf
- OA Status
- green
- Cited By
- 12
- References
- 42
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3035281230
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3035281230Canonical identifier for this work in OpenAlex
- Title
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Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMCWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-12Full publication date if available
- Authors
-
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Vasudevan Srinivas, Bo Pang, Song‐Chun Zhu, Ying WuList of authors in order
- Landing page
-
https://arxiv.org/pdf/2006.06897.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2006.06897.pdfDirect OA link when available
- Concepts
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Markov chain Monte Carlo, Computer science, Sampling (signal processing), Space (punctuation), Simple (philosophy), Flow (mathematics), Artificial neural network, Artificial intelligence, Latent variable, Energy (signal processing), Machine learning, Mathematics, Statistics, Bayesian probability, Computer vision, Filter (signal processing), Geometry, Epistemology, Operating system, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 2, 2021: 5, 2020: 4Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.This | 49, 143 |
| abstract_inverted_index.both | 55 |
| abstract_inverted_index.case | 129 |
| abstract_inverted_index.data | 25, 47, 141 |
| abstract_inverted_index.deep | 40 |
| abstract_inverted_index.form | 102 |
| abstract_inverted_index.loop | 14 |
| abstract_inverted_index.show | 94 |
| abstract_inverted_index.that | 79, 95 |
| abstract_inverted_index.this | 63 |
| abstract_inverted_index.well | 135 |
| abstract_inverted_index.with | 70 |
| abstract_inverted_index.(EBM) | 3 |
| abstract_inverted_index.MCMC, | 133 |
| abstract_inverted_index.inner | 13 |
| abstract_inverted_index.learn | 68 |
| abstract_inverted_index.mixes | 134 |
| abstract_inverted_index.model | 2, 10, 73, 97 |
| abstract_inverted_index.modes | 138 |
| abstract_inverted_index.space | 26, 105 |
| abstract_inverted_index.which | 35, 124 |
| abstract_inverted_index.energy | 33 |
| abstract_inverted_index.highly | 43 |
| abstract_inverted_index.latent | 108, 122 |
| abstract_inverted_index.model, | 113 |
| abstract_inverted_index.model. | 92 |
| abstract_inverted_index.neural | 131 |
| abstract_inverted_index.paper, | 64 |
| abstract_inverted_index.proper | 145 |
| abstract_inverted_index.simple | 101, 127 |
| abstract_inverted_index.space, | 123 |
| abstract_inverted_index.space. | 48, 142 |
| abstract_inverted_index.theory | 57 |
| abstract_inverted_index.because | 31 |
| abstract_inverted_index.enables | 144 |
| abstract_inverted_index.learned | 9 |
| abstract_inverted_index.mixing, | 30 |
| abstract_inverted_index.propose | 66 |
| abstract_inverted_index.serious | 52 |
| abstract_inverted_index.serving | 74 |
| abstract_inverted_index.special | 128 |
| abstract_inverted_index.tilting | 88 |
| abstract_inverted_index.usually | 37 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.Learning | 0 |
| abstract_inverted_index.handicap | 53 |
| abstract_inverted_index.learning | 17, 148 |
| abstract_inverted_index.network, | 41 |
| abstract_inverted_index.practice | 59 |
| abstract_inverted_index.requires | 4 |
| abstract_inverted_index.sampling | 6, 21, 116, 146 |
| abstract_inverted_index.backbone, | 77 |
| abstract_inverted_index.function, | 34 |
| abstract_inverted_index.generally | 28 |
| abstract_inverted_index.transport | 132 |
| abstract_inverted_index.traverses | 137 |
| abstract_inverted_index.variables | 109 |
| abstract_inverted_index.algorithm. | 18 |
| abstract_inverted_index.correction | 84 |
| abstract_inverted_index.flow-based | 72, 91, 112 |
| abstract_inverted_index.exponential | 87 |
| abstract_inverted_index.multi-modal | 44 |
| abstract_inverted_index.energy-based | 1 |
| abstract_inverted_index.parametrized | 38 |
| abstract_inverted_index.particularly | 100 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8899999856948853 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |