Measuring and Harnessing Transference in Multi-Task Learning Article Swipe
Chris Fifty
,
Ehsan Amid
,
Zhe Zhao
,
Tianhe Yu
,
Rohan Anil
,
Chelsea Finn
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.15413
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.15413
Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naive formulations often degrade performance and in particular, identifying the tasks that would benefit from co-training remains a challenging design question. In this paper, we analyze the dynamics of information transfer, or transference, across tasks throughout training. Specifically, we develop a similarity measure that can quantify transference among tasks and use this quantity to both better understand the optimization dynamics of multi-task learning as well as improve overall learning performance. In the latter case, we propose two methods to leverage our transference metric. The first operates at a macro-level by selecting which tasks should train together while the second functions at a micro-level by determining how to combine task gradients at each training step. We find these methods can lead to significant improvement over prior work on three supervised multi-task learning benchmarks and one multi-task reinforcement learning paradigm.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2010.15413
- https://arxiv.org/pdf/2010.15413
- OA Status
- green
- Cited By
- 7
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3094807849
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3094807849Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2010.15413Digital Object Identifier
- Title
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Measuring and Harnessing Transference in Multi-Task LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-10-29Full publication date if available
- Authors
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Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea FinnList of authors in order
- Landing page
-
https://arxiv.org/abs/2010.15413Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2010.15413Direct link to full text PDF
- 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/2010.15413Direct OA link when available
- Concepts
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Leverage (statistics), Computer science, Reinforcement learning, Task (project management), Machine learning, Artificial intelligence, Transfer of learning, Multi-task learning, Metric (unit), Human–computer interaction, Operations management, Economics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2022: 1, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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61Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2931142868, https://openalex.org/W2970803838, https://openalex.org/W2963677766, https://openalex.org/W3209042722, https://openalex.org/W1522301498, https://openalex.org/W2805481182, https://openalex.org/W2890538051, https://openalex.org/W2742079690, https://openalex.org/W2964153283, https://openalex.org/W2982303846, https://openalex.org/W2970470314, https://openalex.org/W2946233749, https://openalex.org/W2884886306, https://openalex.org/W2996490626, https://openalex.org/W2107438106, https://openalex.org/W2549401308, https://openalex.org/W2750384547, https://openalex.org/W2402144811, https://openalex.org/W2219888463, https://openalex.org/W2927589347, https://openalex.org/W2963936326, https://openalex.org/W2112796928, https://openalex.org/W2112093930, https://openalex.org/W2767434619, https://openalex.org/W2504108613, https://openalex.org/W2251324968, https://openalex.org/W3033503043, https://openalex.org/W3092055400, https://openalex.org/W125693051, https://openalex.org/W3048443606, https://openalex.org/W2426267443, https://openalex.org/W2339391301, https://openalex.org/W2895387432, https://openalex.org/W2975638653, https://openalex.org/W2990761674, https://openalex.org/W2913340405, https://openalex.org/W2798512429, https://openalex.org/W2996489182, https://openalex.org/W2951720331, https://openalex.org/W3041133507, https://openalex.org/W2951775809, https://openalex.org/W2194775991, https://openalex.org/W2036043322, https://openalex.org/W3105787868, https://openalex.org/W2795900505, https://openalex.org/W3104240813, https://openalex.org/W2981344907, https://openalex.org/W1525859397, https://openalex.org/W2784596339, https://openalex.org/W2781726626, https://openalex.org/W3032377877, https://openalex.org/W2997359900, https://openalex.org/W2794363191, https://openalex.org/W3092189037, https://openalex.org/W2991309414, https://openalex.org/W1614862348, https://openalex.org/W2624871570, https://openalex.org/W3021037761, https://openalex.org/W1834627138, https://openalex.org/W2792287754, https://openalex.org/W2432541215 |
| referenced_works_count | 61 |
| abstract_inverted_index.a | 36, 59, 106, 120 |
| abstract_inverted_index.In | 40, 89 |
| abstract_inverted_index.We | 133 |
| abstract_inverted_index.as | 82, 84 |
| abstract_inverted_index.at | 105, 119, 129 |
| abstract_inverted_index.by | 6, 108, 122 |
| abstract_inverted_index.in | 25 |
| abstract_inverted_index.of | 13, 47, 79 |
| abstract_inverted_index.on | 145 |
| abstract_inverted_index.or | 50 |
| abstract_inverted_index.to | 9, 72, 97, 125, 139 |
| abstract_inverted_index.we | 43, 57, 93 |
| abstract_inverted_index.The | 102 |
| abstract_inverted_index.and | 24, 68, 151 |
| abstract_inverted_index.can | 2, 63, 137 |
| abstract_inverted_index.how | 124 |
| abstract_inverted_index.one | 7, 152 |
| abstract_inverted_index.our | 99 |
| abstract_inverted_index.the | 11, 28, 45, 76, 90, 116 |
| abstract_inverted_index.two | 95 |
| abstract_inverted_index.use | 69 |
| abstract_inverted_index.both | 73 |
| abstract_inverted_index.each | 130 |
| abstract_inverted_index.find | 134 |
| abstract_inverted_index.from | 33 |
| abstract_inverted_index.lead | 138 |
| abstract_inverted_index.over | 142 |
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| abstract_inverted_index.that | 30, 62 |
| abstract_inverted_index.this | 17, 41, 70 |
| abstract_inverted_index.well | 83 |
| abstract_inverted_index.work | 144 |
| abstract_inverted_index.among | 66 |
| abstract_inverted_index.case, | 92 |
| abstract_inverted_index.first | 103 |
| abstract_inverted_index.naive | 19 |
| abstract_inverted_index.often | 21 |
| abstract_inverted_index.other | 14 |
| abstract_inverted_index.prior | 143 |
| abstract_inverted_index.step. | 132 |
| abstract_inverted_index.tasks | 29, 53, 67, 111 |
| abstract_inverted_index.these | 135 |
| abstract_inverted_index.three | 146 |
| abstract_inverted_index.train | 113 |
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| abstract_inverted_index.while | 115 |
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| abstract_inverted_index.across | 52 |
| abstract_inverted_index.better | 74 |
| abstract_inverted_index.design | 38 |
| abstract_inverted_index.latter | 91 |
| abstract_inverted_index.paper, | 42 |
| abstract_inverted_index.second | 117 |
| abstract_inverted_index.should | 112 |
| abstract_inverted_index.tasks. | 15 |
| abstract_inverted_index.Despite | 16 |
| abstract_inverted_index.analyze | 44 |
| abstract_inverted_index.benefit | 10, 32 |
| abstract_inverted_index.combine | 126 |
| abstract_inverted_index.degrade | 22 |
| abstract_inverted_index.develop | 58 |
| abstract_inverted_index.improve | 85 |
| abstract_inverted_index.learned | 5 |
| abstract_inverted_index.measure | 61 |
| abstract_inverted_index.methods | 96, 136 |
| abstract_inverted_index.metric. | 101 |
| abstract_inverted_index.overall | 86 |
| abstract_inverted_index.propose | 94 |
| abstract_inverted_index.remains | 35 |
| abstract_inverted_index.dynamics | 46, 78 |
| abstract_inverted_index.learning | 1, 81, 87, 149, 155 |
| abstract_inverted_index.leverage | 3, 98 |
| abstract_inverted_index.operates | 104 |
| abstract_inverted_index.quantify | 64 |
| abstract_inverted_index.quantity | 71 |
| abstract_inverted_index.together | 114 |
| abstract_inverted_index.training | 12, 131 |
| abstract_inverted_index.capacity, | 18 |
| abstract_inverted_index.functions | 118 |
| abstract_inverted_index.gradients | 128 |
| abstract_inverted_index.paradigm. | 156 |
| abstract_inverted_index.question. | 39 |
| abstract_inverted_index.selecting | 109 |
| abstract_inverted_index.training. | 55 |
| abstract_inverted_index.transfer, | 49 |
| abstract_inverted_index.Multi-task | 0 |
| abstract_inverted_index.benchmarks | 150 |
| abstract_inverted_index.multi-task | 80, 148, 153 |
| abstract_inverted_index.similarity | 60 |
| abstract_inverted_index.supervised | 147 |
| abstract_inverted_index.throughout | 54 |
| abstract_inverted_index.understand | 75 |
| abstract_inverted_index.challenging | 37 |
| abstract_inverted_index.co-training | 34 |
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| abstract_inverted_index.identifying | 27 |
| abstract_inverted_index.improvement | 141 |
| abstract_inverted_index.information | 4, 48 |
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| abstract_inverted_index.micro-level | 121 |
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| abstract_inverted_index.performance | 23 |
| abstract_inverted_index.significant | 140 |
| abstract_inverted_index.formulations | 20 |
| abstract_inverted_index.optimization | 77 |
| abstract_inverted_index.performance. | 88 |
| abstract_inverted_index.transference | 65, 100 |
| abstract_inverted_index.Specifically, | 56 |
| abstract_inverted_index.reinforcement | 154 |
| abstract_inverted_index.transference, | 51 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |