Simple, Distributed, and Accelerated Probabilistic Programming Article Swipe
Dustin Tran
,
Matthew W. Hoffman
,
Dave Moore
,
Christopher Suter
,
Vasudevan Srinivas
,
Alexey Radul
·
YOU?
·
· 2018
· Open Access
·
YOU?
·
· 2018
· Open Access
·
We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction—the random variable. Our lightweight implementation in TensorFlow enables numerous applications: a model-parallel variational auto-encoder (VAE) with 2nd-generation tensor processing units (TPUv2s); a data-parallel autoregressive model (Image Transformer) with TPUv2s; and multi-GPU No-U-Turn Sampler (NUTS). For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips. With NUTS, we see a 100x speedup on GPUs over Stan and 37x over PyMC3.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1811.02091.pdf
- OA Status
- green
- Cited By
- 30
- Related Works
- 20
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2963410375Canonical identifier for this work in OpenAlex
- Title
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Simple, Distributed, and Accelerated Probabilistic ProgrammingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
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Dustin Tran, Matthew W. Hoffman, Dave Moore, Christopher Suter, Vasudevan Srinivas, Alexey RadulList of authors in order
- Landing page
-
https://arxiv.org/pdf/1811.02091.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/1811.02091.pdfDirect OA link when available
- Concepts
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Speedup, Computer science, Probabilistic logic, Embedding, Parallel computing, Programming paradigm, Encoder, Autoregressive model, Abstraction, Artificial intelligence, Theoretical computer science, Algorithm, Programming language, Mathematics, Philosophy, Econometrics, Operating system, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 11, 2020: 9, 2019: 8, 2018: 1Per-year citation counts (last 5 years)
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 55, 68, 98 |
| abstract_inverted_index.for | 6 |
| abstract_inverted_index.our | 74 |
| abstract_inverted_index.see | 90 |
| abstract_inverted_index.100x | 92 |
| abstract_inverted_index.GPUs | 95 |
| abstract_inverted_index.Stan | 97 |
| abstract_inverted_index.With | 87 |
| abstract_inverted_index.both | 61 |
| abstract_inverted_index.deep | 12 |
| abstract_inverted_index.down | 21 |
| abstract_inverted_index.from | 81 |
| abstract_inverted_index.over | 96, 100 |
| abstract_inverted_index.with | 41, 53 |
| abstract_inverted_index.(VAE) | 40 |
| abstract_inverted_index.64x64 | 66 |
| abstract_inverted_index.Image | 69 |
| abstract_inverted_index.NUTS, | 88 |
| abstract_inverted_index.TPUv2 | 85 |
| abstract_inverted_index.model | 50 |
| abstract_inverted_index.units | 45 |
| abstract_inverted_index.(Image | 51 |
| abstract_inverted_index.PyMC3. | 101 |
| abstract_inverted_index.chips. | 86 |
| abstract_inverted_index.linear | 79 |
| abstract_inverted_index.random | 26 |
| abstract_inverted_index.single | 24 |
| abstract_inverted_index.tensor | 43 |
| abstract_inverted_index.(NUTS). | 59 |
| abstract_inverted_index.256x256 | 72 |
| abstract_inverted_index.Sampler | 58 |
| abstract_inverted_index.TPUv2s; | 54 |
| abstract_inverted_index.distill | 18 |
| abstract_inverted_index.enables | 33 |
| abstract_inverted_index.optimal | 78 |
| abstract_inverted_index.simple, | 3 |
| abstract_inverted_index.speedup | 80, 93 |
| abstract_inverted_index.ImageNet | 67 |
| abstract_inverted_index.achieves | 76 |
| abstract_inverted_index.approach | 5, 75 |
| abstract_inverted_index.describe | 1 |
| abstract_inverted_index.learning | 13 |
| abstract_inverted_index.numerous | 34 |
| abstract_inverted_index.(TPUv2s); | 46 |
| abstract_inverted_index.No-U-Turn | 57 |
| abstract_inverted_index.embedding | 7 |
| abstract_inverted_index.low-level | 4 |
| abstract_inverted_index.multi-GPU | 56 |
| abstract_inverted_index.variable. | 27 |
| abstract_inverted_index.CelebA-HQ, | 73 |
| abstract_inverted_index.TensorFlow | 32 |
| abstract_inverted_index.ecosystem. | 14 |
| abstract_inverted_index.processing | 44 |
| abstract_inverted_index.Transformer | 70 |
| abstract_inverted_index.lightweight | 29 |
| abstract_inverted_index.particular, | 16 |
| abstract_inverted_index.programming | 9, 20 |
| abstract_inverted_index.variational | 38 |
| abstract_inverted_index.Transformer) | 52 |
| abstract_inverted_index.auto-encoder | 39 |
| abstract_inverted_index.applications: | 35 |
| abstract_inverted_index.data-parallel | 48 |
| abstract_inverted_index.probabilistic | 8, 19 |
| abstract_inverted_index.2nd-generation | 42 |
| abstract_inverted_index.autoregressive | 49 |
| abstract_inverted_index.implementation | 30 |
| abstract_inverted_index.model-parallel | 37 |
| abstract_inverted_index.state-of-the-art | 63 |
| abstract_inverted_index.abstraction—the | 25 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.96004162 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |