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Multitask Learning
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“There are two main goals in continual learning: maintaining stability and maintaining plasticity88–91.”
From Loss of plasticity in deep continual learning (2024) · cites this paper as [88]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“pretraining followed by task-specific fine-tuning can improve generalization (Caruana, 1997)”
From Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution (2025) · cites this paper as Caruana, 1997
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“These studies hint that a common representation of lensing data can benefit various tasks”
From Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution (2025) · cites this paper as Caruana, 1997
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The motivation for the proposed architecture is that the multi-task learning (Caruana, 1997) of related tasks could improve the generalization capability by suppressing the shared encoder from learning any taskspecific features.”
From Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap (2024) · cites this paper as Caruana, 1997
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“Inspired by Caruana, they employed shared layers to extract hidden features of different battery states and task-specific layers to achieve accurate multi-state monitoring[110].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [110]
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“For this reason, ILP naturally supports lifelong (Silver et al., 2013), multi-task (Caruana, 1997), and transfer learning (Torrey & Shavlik, 2009), which are considered essential for human-like AI (Lake et al., 2016).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Caruana, 1997
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“Vision Transformers are commonly evaluated using the sliding window procedure from MMSegmentation [59] (e.g., [10, 101, 103]).”
From MultiMAE: Multi-modal Multi-task Masked Autoencoders (2022) · cites this paper as [10]
Experimental — accuracy not yet measuredIn the citing paper
“Within this formulation, our multi-task supervised contrastive framework provides a unified optimization process in which the classification and contrastive objectives jointly guide the encoder to capture activity semantics while suppressing user-specific variations [37].”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [37]
In the citing paper
“In addition, using multi-task learning 147 to train models to perform a variety of tasks rather than one narrowly defined task, such as multi-cancer detection from histopathology images 148 , makes them more generally useful and often more robust.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [147]
In the citing paper
“To increase the robustness Multi-Task Learning (MTL) uses the information in training data as an inductive bias to perform multiple tasks in parallel, and the learning of one task improves the understanding of others through shared representations [82].”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [82]
In the citing paper
“Hard parameter sharing [110]: It consists of extracting a generic representation for different tasks using the same parameters. It is usually applied to avoid overfitting problems.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [110]
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3 of the 67 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Caruana, R. (1994).”Multitask Connectionist Learning,” Proceedings of the 1993 Connectionist Models Summer School, pp. 372-379.
- Caruana, R. (1997). “Multitask Learning,” Ph.D. Thesis, School of Computer Science, Carnegie Mellon University.
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Metadata
- Authors
- Caruana, R.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Caruana, Rich
- ORCID
- Caruana, Rich
- Venue
- Machine Learning
- Published
- 1997-07
- Volume
- 28
- Issue
- 1
- Pages
- 41-75
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2913340405
- Cited by
- 6,636
- References
- 67
- Citations by year
- 2026: 532 · 2025: 617 · 2024: 510 · 2023: 505 · 2022: 565 · 2021: 803 · 2020: 767 · 2019: 563 · 2018: 323 · 2017: 259 · 2016: 188 · 2015: 151 · 2014: 124 · 2013: 89 · 2012: 136
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- National Science Foundation (9315428)
- University of Pittsburgh
- Advanced Research Projects Agency
- 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 › Reinforcement Learning in Robotics
- Keywords
- multi-task learning, inductive transfer, inductive bias, shared representations, task relatedness, k-nearest neighbors, kernel regression, case-based learning, decision tree
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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