The DOI matches its Crossref record.
A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling
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Citations
Cited by
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Practical Recommendations for Gradient-Based Training of Deep Architectures
Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259
Taskonomy: Disentangling Task Transfer Learning
Zamir, A. R., Sax, A., Shen, W., Guibas, L., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00391 · Cited by 1,128
Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
Taylor, S., Jaques, N., Nosakhare, E., Sano, A., & Picard, R. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2784832 · Cited by 251
Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison
Palsson, B., Sveinsson, J. R., & Ulfarsson, M. O. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3140154 · Cited by 107
Varnek, A., Gaudin, C., Marcou, G., Baskin, I., Pandey, A. K., & Tetko, I. V. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002914 · Cited by 86
Automated Emerging Cyber Threat Identification and Profiling Based on Natural Language Processing
Marinho, R., & Holanda, R. (2023) · IEEE Access · doi:10.1109/access.2023.3260020 · Cited by 50
IF-CNN: Image-Aware Inference Framework for CNN With the Collaboration of Mobile Devices and Cloud
Shu, G., Liu, W., Zheng, X., & Li, J. (2018) · IEEE Access · doi:10.1109/access.2018.2880196 · Cited by 30
Strategic cognitive sequencing: a computational cognitive neuroscience approach
Herd, S. A., Krueger, K. A., Kriete, T. E., Huang, T.-R., Hazy, T. E., & O'Reilly, R. C. (2013) · Computational Intelligence and Neuroscience · doi:10.1155/2013/149329 · Cited by 20
Hybrid multitask learning reveals sequence features driving specificity in the CRISPR/Cas9 system
Vora, D. S., Yadav, S., & Sundar, D. (2023) · Biomolecules · doi:10.3390/biom13040641 · Cited by 17
Metadata
- Authors
- Baxter, J.
- Venue
- Machine Learning
- Published
- 1997-07
- Volume
- 28
- Issue
- 1
- Pages
- 7-39
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2143419558
- Cited by
- 462
- References
- 0
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
