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Multi-task learning (MTL) aims to improve the generalization of several related tasks by learning them jointly.
On deriving the inverse of a sum of matrices
Henderson, H. V. and Searle, S. R · 1981
Earlier work this paper cites.
Multitask learning
Caruana, R · 1997
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Theoretical models of learning to learn
Baxter, J · 1998
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Learning to learn: Introduction and overview
Thrun, S. and Pratt, L · 1998
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Multi-task feature learning
Evgeniou, A. and Pontil, M · 2007
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Convex multi-task feature learning
Argyriou, A., Evgeniou, T., and Pontil, M · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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A convex formulation for learning task relationships in multi-task learning
Zhang, Y. and Yeung, D. Y · 2010
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Facial landmark detection by deep multi-task learning
Zhang, Z., Luo, P., Loy, C. C., and Tang, X · 2014
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Multi-task learning for multiple language translation
Dong, D., Wu, H., He, W., Yu, D., and Wang, H · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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The benefit of multitask representation learning
Maurer, A., Pontil, M., and Romera-Paredes, B · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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An overview of multi-task learning in deep neural networks, 2017
Ruder, S · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Earlier work this paper cites.
Recasting gradient-based meta-learning as hierarchical bayes
Grant, E., Finn, C., Levine, S., Darrell, T., and Griffiths, T · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
Cited alongside, same era.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Kendall, A., Gal, Y., and Cipolla, R · 2018
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Deep neural networks as gaussian processes
Lee, J., Sohl-dickstein, J., Pennington, J., Novak, R., Schoenholz, S., and Bahri, Y · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., Rodríguez López, P., and Lacoste, A · 2018
Cited alongside, same era.
Meta-learning for semi-supervised few-shot classification
Ren, M., Ravi, S., Triantafillou, E., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S · 2018
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A convergence theory for deep learning via over-parameterization
Finite depth and width corrections to the neural tangent kernel
Hanin, B. and Nica, M · 2020
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Meta-learning in neural networks: A survey, 2020
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A · 2020
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Meta learning for end-to-end low-resource speech recognition
Hsu, J.-Y., Chen, Y.-J., and Lee, H.-y · 2020
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Biased stochastic gradient descent for conditional stochastic optimization
Hu, Y., Zhang, S., Chen, X., and He, N · 2020
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Multi-step model-agnostic meta-learning: Convergence and improved algorithms
Ji, K., Yang, J., and Liang, Y · 2020
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Allen-Zhu, Z., Li, Y., and Song, Z · 2019
Cited alongside, same era.
How to train your MAML
Antoniou, A., Edwards, H., and Storkey, A · 2019
Cited alongside, same era.
On exact computation with an infinitely wide neural net
Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R., and Wang, R · 2019
Cited alongside, same era.
Provable guarantees for gradient-based meta-learning
Balcan, M.-F., Khodak, M., and Talwalkar, A · 2019
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P., and Vedaldi, A · 2019
Cited alongside, same era.
Gradient descent finds global minima of deep neural networks
Du, S. S., Lee, J. D., Li, H., Wang, L., and Zhai, X · 2019
Cited alongside, same era.
Online meta-learning
Finn, C., Rajeswaran, A., Kakade, S., and Levine, S · 2019
Cited alongside, same era.
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J · 2020
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Neural tangents: Fast and easy infinite neural networks in python
Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 2020
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Raghu, A., Raghu, M., Bengio, S., and Vinyals, O · 2020
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A sample complexity separation between non-convex and convex meta-learning, 2020
Saunshi, N., Zhang, Y., Khodak, M., and Arora, S · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., and Isola, P · 2020
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On the theory of transfer learning: The importance of task diversity
Tripuraneni, N., Jordan, M., and Jin, C · 2020
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Global convergence and induced kernels of gradient-based meta-learning with neural nets
Wang, H., Sun, R., and Li, B · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Wang, T. and Isola, P · 2020
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Disentangling trainability and generalization in deep learning
Xiao, L., Pennington, J., and Schoenholz, S. S · 2020
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Meta learning in the continuous time limit
Xu, R., Chen, L., and Karbasi, A · 2020
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Efficient multitask feature and relationship learning
Zhao, H., Stretcu, O., Smola, A. J., and Gordon, G. J · 2020
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How important is the train-validation split in meta-learning?
Bai, Y., Chen, M., Zhou, P., Zhao, T., Lee, J. D., Kakade, S. M., Wang, H., and Xiong, C · 2021
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Few-shot learning via learning the representation, provably
Du, S. S., Hu, W., Kakade, S. M., Lee, J. D., and Lei, Q · 2021
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Evaluation of neural architectures trained with square loss vs cross-entropy in classification tasks
Hui, L. and Belkin, M · 2021
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To learn effective features: Understanding the task-specific adaptation of {maml}, 2021
Lin, Z., Zhao, Z., Zhang, Z., Baoxing, H., and Yuan, J · 2021
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An overview of multi-task learning
Zhang, Y. and Yang, Q · 2095
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