Efficient and Robust Machine Learning for Real-World Systems Article Swipe
Franz Pernkopf
,
Wolfgang Roth
,
Matthias Zöhrer
,
Lukas Pfeifenberger
,
Günther Schindler
,
Holger Fröning
,
Sebastian Tschiatschek
,
Robert Peharz
,
Matthew Mattina
,
Zoubin Ghahramani
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1812.02240
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1812.02240
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\ the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1812.02240
- https://arxiv.org/pdf/1812.02240
- OA Status
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- Cited By
- 1
- References
- 92
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W2902232562Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.1812.02240Digital Object Identifier
- Title
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Efficient and Robust Machine Learning for Real-World SystemsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-12-05Full publication date if available
- Authors
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Franz Pernkopf, Wolfgang Roth, Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin GhahramaniList of authors in order
- Landing page
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https://arxiv.org/abs/1812.02240Publisher landing page
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https://arxiv.org/pdf/1812.02240Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1812.02240Direct OA link when available
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Computer science, Machine learning, Artificial intelligence, Probabilistic logic, Resource (disambiguation), Memory footprint, Deep learning, Operating system, Computer networkTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.own | 108 |
| abstract_inverted_index.the | 14, 17, 20, 61, 81, 97, 115, 146, 150, 245 |
| abstract_inverted_index.top | 45 |
| abstract_inverted_index.both | 200 |
| abstract_inverted_index.deep | 169 |
| abstract_inverted_index.fuel | 19 |
| abstract_inverted_index.into | 134 |
| abstract_inverted_index.most | 207 |
| abstract_inverted_index.over | 106 |
| abstract_inverted_index.size | 178 |
| abstract_inverted_index.that | 237 |
| abstract_inverted_index.them | 221 |
| abstract_inverted_index.this | 139 |
| abstract_inverted_index.used | 197 |
| abstract_inverted_index.way, | 238 |
| abstract_inverted_index.ways | 215 |
| abstract_inverted_index.well | 89 |
| abstract_inverted_index.with | 172, 222 |
| abstract_inverted_index.First | 160 |
| abstract_inverted_index.These | 26, 110, 184 |
| abstract_inverted_index.While | 0 |
| abstract_inverted_index.among | 114 |
| abstract_inverted_index.being | 91 |
| abstract_inverted_index.data, | 87 |
| abstract_inverted_index.day's | 136 |
| abstract_inverted_index.every | 135 |
| abstract_inverted_index.focus | 173 |
| abstract_inverted_index.i.e.\ | 96 |
| abstract_inverted_index.major | 116 |
| abstract_inverted_index.means | 232 |
| abstract_inverted_index.model | 177 |
| abstract_inverted_index.serve | 228 |
| abstract_inverted_index.state | 148 |
| abstract_inverted_index.task, | 8 |
| abstract_inverted_index.terms | 39 |
| abstract_inverted_index.their | 214 |
| abstract_inverted_index.these | 157, 229 |
| abstract_inverted_index.this, | 47 |
| abstract_inverted_index.treat | 52, 217 |
| abstract_inverted_index.which | 226 |
| abstract_inverted_index.chosen | 31 |
| abstract_inverted_index.during | 189 |
| abstract_inverted_index.ensure | 126 |
| abstract_inverted_index.manner | 57 |
| abstract_inverted_index.memory | 204 |
| abstract_inverted_index.neural | 170, 209 |
| abstract_inverted_index.reduce | 199 |
| abstract_inverted_index.review | 165 |
| abstract_inverted_index.robust | 79, 249 |
| abstract_inverted_index.should | 99 |
| abstract_inverted_index.smooth | 128 |
| abstract_inverted_index.system | 75, 98 |
| abstract_inverted_index.vision | 15 |
| abstract_inverted_index.widely | 196 |
| abstract_inverted_index.`aware' | 92 |
| abstract_inverted_index.applied | 188 |
| abstract_inverted_index.between | 33 |
| abstract_inverted_index.complex | 111 |
| abstract_inverted_index.crucial | 50 |
| abstract_inverted_index.current | 119, 147, 246 |
| abstract_inverted_index.demands | 112 |
| abstract_inverted_index.energy. | 43 |
| abstract_inverted_index.limited | 212 |
| abstract_inverted_index.limits, | 95 |
| abstract_inverted_index.machine | 1, 65, 120, 131, 153, 252 |
| abstract_inverted_index.models, | 225 |
| abstract_inverted_index.provide | 102, 142, 162, 240 |
| abstract_inverted_index.readily | 227 |
| abstract_inverted_index.reduced | 182 |
| abstract_inverted_index.require | 28 |
| abstract_inverted_index.article, | 140 |
| abstract_inverted_index.contrast | 220 |
| abstract_inverted_index.embedded | 9 |
| abstract_inverted_index.estimate | 105 |
| abstract_inverted_index.interest | 21 |
| abstract_inverted_index.learning | 2, 66, 121, 132, 154, 253 |
| abstract_inverted_index.maintain | 100 |
| abstract_inverted_index.networks | 171, 210 |
| abstract_inverted_index.outliers | 84 |
| abstract_inverted_index.overview | 144, 243 |
| abstract_inverted_index.presence | 82 |
| abstract_inverted_index.research | 122 |
| abstract_inverted_index.resource | 6, 23, 36 |
| abstract_inverted_index.simplest | 62 |
| abstract_inverted_index.systems, | 10 |
| abstract_inverted_index.systems. | 67, 256 |
| abstract_inverted_index.training | 190 |
| abstract_inverted_index.carefully | 30 |
| abstract_inverted_index.corrupted | 86 |
| abstract_inverted_index.efficient | 24, 251 |
| abstract_inverted_index.extensive | 242 |
| abstract_inverted_index.graphical | 224 |
| abstract_inverted_index.intensive | 7 |
| abstract_inverted_index.trade-off | 32 |
| abstract_inverted_index.approaches | 27 |
| abstract_inverted_index.autonomous | 11 |
| abstract_inverted_index.challenges | 117 |
| abstract_inverted_index.complexity | 202 |
| abstract_inverted_index.consistent | 56 |
| abstract_inverted_index.desiderata | 230 |
| abstract_inverted_index.footprint. | 205 |
| abstract_inverted_index.inference. | 235 |
| abstract_inverted_index.navigation | 12 |
| abstract_inverted_index.precision. | 183 |
| abstract_inverted_index.real-world | 74, 158, 255 |
| abstract_inverted_index.reduction, | 179 |
| abstract_inverted_index.techniques | 155, 175, 185 |
| abstract_inverted_index.technology | 133 |
| abstract_inverted_index.transition | 129 |
| abstract_inverted_index.(practical) | 208 |
| abstract_inverted_index.approaches. | 25 |
| abstract_inverted_index.compression | 180 |
| abstract_inverted_index.computation | 41 |
| abstract_inverted_index.consumption | 37 |
| abstract_inverted_index.desideratum | 71 |
| abstract_inverted_index.particular, | 69 |
| abstract_inverted_index.performance | 34 |
| abstract_inverted_index.uncertainty | 53, 104 |
| abstract_inverted_index.applications | 63 |
| abstract_inverted_index.facilitating | 156 |
| abstract_inverted_index.predictions. | 109 |
| abstract_inverted_index.uncertainty, | 218 |
| abstract_inverted_index.applications. | 137 |
| abstract_inverted_index.comprehensive | 164 |
| abstract_inverted_index.computational | 201 |
| abstract_inverted_index.probabilistic | 223, 234 |
| abstract_inverted_index.requirements. | 159 |
| abstract_inverted_index.traditionally | 4 |
| abstract_inverted_index.post-processing | 193 |
| abstract_inverted_index.state-of-the-art | 247 |
| abstract_inverted_index.Internet-of-Things | 18 |
| abstract_inverted_index.resource-efficiency | 167 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile |