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“Multi-task (MT) learning 8 was proposed partially to deal with data sparsity problem, which is commonly encountered in QSAR applications.”
From Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks (2018) · cites this paper as [8]
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Smet, S. D., Scheeres, D. J., & Parker, J. S. (2019) · The Journal of the Astronautical Sciences · doi:10.1007/s40295-018-00149-w · Cited by 4
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Yu, M., Zheng, Z., Li, Q., Wu, F., & Zheng, J. (2022) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00013 · Cited by 4
Metadata
- Authors
- Caruana, R.
- Corresponding author
- Caruana, Rich
- ORCID
- Caruana, Rich
- Venue
- Learning to Learn
- Publisher
- Springer US
- Published
- 1998
- Pages
- 95-133
- OpenAlex
- W4239943352
- Cited by
- 577
- References
- 52
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 39 · 2023: 106 · 2022: 98 · 2021: 31 · 2020: 27 · 2019: 52 · 2018: 99 · 2017: 63 · 2016: 9 · 2015: 14 · 2014: 4 · 2013: 1 · 2012: 1
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- multi-task learning, inductive transfer, inductive bias, shared representations, task relatedness, k-nearest neighbors, kernel regression, decision tree, generalization
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