Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning Article Swipe
Ankur Mallick
,
Chaitanya Dwivedi
,
Bhavya Kailkhura
,
Gauri Joshi
,
T. Yong-Jin Han
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.10800
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.10800
While Deep Neural Networks (DNNs) achieve state-of-the-art accuracy in various applications, they often fall short in accurately estimating their predictive uncertainty and, in turn, fail to recognize when these predictions may be wrong. Several uncertainty-aware models, such as Bayesian Neural Network (BNNs) and Deep Ensembles have been proposed in the literature for quantifying predictive uncertainty. However, research in this area has been largely confined to the big data regime. In this work, we show that the uncertainty estimation capability of state-of-the-art BNNs and Deep Ensemble models degrades significantly when the amount of training data is small. To address the issue of accurate uncertainty estimation in the small-data regime, we propose a probabilistic generalization of the popular sample-efficient non-parametric kNN approach. Our approach enables deep kNN classifier to accurately quantify underlying uncertainties in its prediction. We demonstrate the usefulness of the proposed approach by achieving superior uncertainty quantification as compared to state-of-the-art on a real-world application of COVID-19 diagnosis from chest X-Rays. Our code is available at https://github.com/ankurmallick/sample-efficient-uq
Related Topics
Concepts
Computer science
Artificial intelligence
Machine learning
Probabilistic logic
Uncertainty quantification
Deep learning
Parametric statistics
Artificial neural network
Deep neural networks
Sample (material)
Classifier (UML)
Bayesian probability
Data mining
Statistics
Mathematics
Chemistry
Chromatography
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.10800
- https://arxiv.org/pdf/2007.10800
- OA Status
- green
- Cited By
- 4
- References
- 21
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3044879143
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3044879143Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2007.10800Digital Object Identifier
- Title
-
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-18Full publication date if available
- Authors
-
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin HanList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.10800Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.10800Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2007.10800Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Machine learning, Probabilistic logic, Uncertainty quantification, Deep learning, Parametric statistics, Artificial neural network, Deep neural networks, Sample (material), Classifier (UML), Bayesian probability, Data mining, Statistics, Mathematics, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 51 |
| abstract_inverted_index.has | 60 |
| abstract_inverted_index.its | 132 |
| abstract_inverted_index.kNN | 118, 124 |
| abstract_inverted_index.may | 30 |
| abstract_inverted_index.the | 49, 65, 75, 89, 98, 105, 114, 136, 139 |
| abstract_inverted_index.BNNs | 81 |
| abstract_inverted_index.Deep | 1, 43, 83 |
| abstract_inverted_index.and, | 21 |
| abstract_inverted_index.area | 59 |
| abstract_inverted_index.been | 46, 61 |
| abstract_inverted_index.code | 162 |
| abstract_inverted_index.data | 67, 93 |
| abstract_inverted_index.deep | 123 |
| abstract_inverted_index.fail | 24 |
| abstract_inverted_index.fall | 13 |
| abstract_inverted_index.from | 158 |
| abstract_inverted_index.have | 45 |
| abstract_inverted_index.show | 73 |
| abstract_inverted_index.such | 36 |
| abstract_inverted_index.that | 74 |
| abstract_inverted_index.they | 11 |
| abstract_inverted_index.this | 58, 70 |
| abstract_inverted_index.when | 27, 88 |
| abstract_inverted_index.While | 0 |
| abstract_inverted_index.chest | 159 |
| abstract_inverted_index.issue | 99 |
| abstract_inverted_index.often | 12 |
| abstract_inverted_index.short | 14 |
| abstract_inverted_index.their | 18 |
| abstract_inverted_index.these | 28 |
| abstract_inverted_index.turn, | 23 |
| abstract_inverted_index.work, | 71 |
| abstract_inverted_index.(BNNs) | 41 |
| abstract_inverted_index.(DNNs) | 4 |
| abstract_inverted_index.Neural | 2, 39 |
| abstract_inverted_index.amount | 90 |
| abstract_inverted_index.models | 85 |
| abstract_inverted_index.small. | 95 |
| abstract_inverted_index.wrong. | 32 |
| abstract_inverted_index.Network | 40 |
| abstract_inverted_index.Several | 33 |
| abstract_inverted_index.X-Rays. | 160 |
| abstract_inverted_index.achieve | 5 |
| abstract_inverted_index.address | 97 |
| abstract_inverted_index.enables | 122 |
| abstract_inverted_index.largely | 62 |
| abstract_inverted_index.models, | 35 |
| abstract_inverted_index.popular | 115 |
| abstract_inverted_index.propose | 109 |
| abstract_inverted_index.regime, | 107 |
| abstract_inverted_index.regime. | 68 |
| abstract_inverted_index.various | 9 |
| abstract_inverted_index.Bayesian | 38 |
| abstract_inverted_index.COVID-19 | 156 |
| abstract_inverted_index.Ensemble | 84 |
| abstract_inverted_index.However, | 55 |
| abstract_inverted_index.Networks | 3 |
| abstract_inverted_index.accuracy | 7 |
| abstract_inverted_index.accurate | 101 |
| abstract_inverted_index.approach | 121, 141 |
| abstract_inverted_index.compared | 148 |
| abstract_inverted_index.confined | 63 |
| abstract_inverted_index.degrades | 86 |
| abstract_inverted_index.proposed | 47, 140 |
| abstract_inverted_index.quantify | 128 |
| abstract_inverted_index.research | 56 |
| abstract_inverted_index.superior | 144 |
| abstract_inverted_index.training | 92 |
| abstract_inverted_index.Ensembles | 44 |
| abstract_inverted_index.achieving | 143 |
| abstract_inverted_index.approach. | 119 |
| abstract_inverted_index.available | 164 |
| abstract_inverted_index.diagnosis | 157 |
| abstract_inverted_index.recognize | 26 |
| abstract_inverted_index.accurately | 16, 127 |
| abstract_inverted_index.capability | 78 |
| abstract_inverted_index.classifier | 125 |
| abstract_inverted_index.estimating | 17 |
| abstract_inverted_index.estimation | 77, 103 |
| abstract_inverted_index.literature | 50 |
| abstract_inverted_index.predictive | 19, 53 |
| abstract_inverted_index.real-world | 153 |
| abstract_inverted_index.small-data | 106 |
| abstract_inverted_index.underlying | 129 |
| abstract_inverted_index.usefulness | 137 |
| abstract_inverted_index.application | 154 |
| abstract_inverted_index.demonstrate | 135 |
| abstract_inverted_index.prediction. | 133 |
| abstract_inverted_index.predictions | 29 |
| abstract_inverted_index.quantifying | 52 |
| abstract_inverted_index.uncertainty | 20, 76, 102, 145 |
| abstract_inverted_index.uncertainty. | 54 |
| abstract_inverted_index.applications, | 10 |
| abstract_inverted_index.probabilistic | 111 |
| abstract_inverted_index.significantly | 87 |
| abstract_inverted_index.uncertainties | 130 |
| abstract_inverted_index.generalization | 112 |
| abstract_inverted_index.non-parametric | 117 |
| abstract_inverted_index.quantification | 146 |
| abstract_inverted_index.sample-efficient | 116 |
| abstract_inverted_index.state-of-the-art | 6, 80, 150 |
| abstract_inverted_index.uncertainty-aware | 34 |
| abstract_inverted_index.https://github.com/ankurmallick/sample-efficient-uq | 166 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |