-
3
-
-
85083954262
-
Continuous adaptation via metalearning in nonstationary and competitive environments
-
M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel. 2018. Continuous adaptation via metalearning in nonstationary and competitive environments. In Proceedings of the International Conference on Learning Representations.
-
(2018)
Proceedings of the International Conference on Learning Representations
-
-
Al-Shedivat, M.1
Bansal, T.2
Burda, Y.3
Sutskever, I.4
Mordatch, I.5
Abbeel, P.6
-
4
-
-
85026486382
-
Low data drug discovery with one-shot learning
-
2017
-
H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. 2017. Low data drug discovery with one-shot learning. ACS Central Sci. 3, 4 (2017), 283-293.
-
(2017)
Acs Central Sci.
, vol.3
, Issue.4
, pp. 283-293
-
-
Altae-Tran, H.1
Ramsundar, B.2
Pappu, A.S.3
Pande, V.4
-
5
-
-
85019172761
-
Learning to learn by gradient descent by gradient descent
-
MIT Press
-
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas. 2016. Learning to learn by gradient descent by gradient descent. In Advances in Neural Information Processing Systems. MIT Press, 3981-3989.
-
(2016)
Advances in Neural Information Processing Systems
, pp. 3981-3989
-
-
Andrychowicz, M.1
Denil, M.2
Gomez, S.3
Hoffman, M.W.4
Pfau, D.5
Schaul, T.6
De Freitas, N.7
-
6
-
-
85064829543
-
Neural voice cloning with a few samples
-
MIT Press
-
S. Arik, J. Chen, K. Peng,W. Ping, and Y. Zhou. 2018. Neural voice cloning with a few samples. In Advances in Neural Information Processing Systems. MIT Press, 10019-10029.
-
(2018)
Advances in Neural Information Processing Systems
, pp. 10019-10029
-
-
Arik, S.1
Chen, J.2
Peng, K.3
Ping, W.4
Zhou, Y.5
-
7
-
-
85062864157
-
Multi-content GAN for few-shot fontstyle transfer
-
S. Azadi, M. Fisher, V. G. Kim, Z. Wang, E. Shechtman, and T. Darrell. 2018. Multi-content GAN for few-shot fontstyle transfer. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 7564-7573.
-
(2018)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 7564-7573
-
-
Azadi, S.1
Fisher, M.2
Kim, V.G.3
Wang, Z.4
Shechtman, E.5
Darrell, T.6
-
14
-
-
85018918773
-
Learning feed-forward one-shot learners
-
MIT Press
-
L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi. 2016. Learning feed-forward one-shot learners. In Advances in Neural Information Processing Systems. MIT Press, 523-531.
-
(2016)
Advances in Neural Information Processing Systems
, pp. 523-531
-
-
Bertinetto, L.1
Henriques, J.F.2
Valmadre, J.3
Torr, P.4
Vedaldi, A.5
-
16
-
-
85161961443
-
Learning bounds for domain adaptation
-
MIT Press
-
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman. 2008. Learning bounds for domain adaptation. In Advances in Neural Information Processing Systems. MIT Press, 129-136.
-
(2008)
Advances in Neural Information Processing Systems
, pp. 129-136
-
-
Blitzer, J.1
Crammer, K.2
Kulesza, A.3
Pereira, F.4
Wortman, J.5
-
18
-
-
85046649212
-
Optimization methods for large-scale machine learning
-
2018
-
L. Bottou, F. E. Curtis, and J. Nocedal. 2018. Optimization methods for large-scale machine learning. SIAM Rev. 60, 2 (2018), 223-311.
-
(2018)
Siam Rev.
, vol.60
, Issue.2
, pp. 223-311
-
-
Bottou, L.1
Curtis, F.E.2
Nocedal, J.3
-
20
-
-
0005594495
-
Signature verification using a "siamese" time delay neural network
-
MIT Press
-
J. Bromley, I. Guyon, Y. LeCun, E. Sackinger, and R. Shah. 1994. Signature verification using a "siamese" time delay neural network. In Advances in Neural Information Processing Systems. MIT Press, 737-744.
-
(1994)
Advances in Neural Information Processing Systems
, pp. 737-744
-
-
Bromley, J.1
Guyon, I.2
LeCun, Y.3
Sackinger, E.4
Shah, R.5
-
21
-
-
85041926984
-
One-shot video object segmentation
-
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixe, D. Cremers, and L. Van Gool. 2017. One-shot video object segmentation. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 221-230.
-
(2017)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 221-230
-
-
Caelles, S.1
Maninis, K.-K.2
Pont-Tuset, J.3
Leal-Taixe, L.4
Cremers, D.5
Van Gool, L.6
-
22
-
-
85062702008
-
Memory matching networks for one-shot image recognition
-
Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei. 2018. Memory matching networks for one-shot image recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 4080-4088.
-
(2018)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 4080-4088
-
-
Cai, Q.1
Pan, Y.2
Yao, T.3
Yan, C.4
Mei, T.5
-
23
-
-
0031189914
-
Multitask learning
-
1997
-
R. Caruana. 1997. Multitask learning. Mach. Learn. 28, 1 (1997), 41-75.
-
(1997)
Mach. Learn.
, vol.28
, Issue.1
, pp. 41-75
-
-
Caruana, R.1
-
25
-
-
85090173604
-
Meta-learning languageguided policy learning
-
J. D. Co-Reyes, A. Gupta, S. Sanjeev, N. Altieri, J. DeNero, P. Abbeel, and S. Levine. 2019. Meta-learning languageguided policy learning. In Proceedings of the International Conference on Learning Representations.
-
(2019)
Proceedings of the International Conference on Learning Representations
-
-
Co-Reyes, J.D.1
Gupta, A.2
Sanjeev, S.3
Altieri, N.4
DeNero, J.5
Abbeel, P.6
Levine, S.7
-
27
-
-
85078722683
-
AutoAugment: Learning augmentation policies from data
-
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. 2019. AutoAugment: Learning augmentation policies from data. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 113-123.
-
(2019)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 113-123
-
-
Cubuk, E.D.1
Zoph, B.2
Mane, D.3
Vasudevan, V.4
Le, Q.V.5
-
30
-
-
72249100259
-
ImageNet: A large-scale hierarchical image database
-
J. Deng,W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 248-255.
-
(2009)
Proceedings of the Conference on Computer Vision Pattern Recognition
, pp. 248-255
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, L.-J.4
Li, K.5
Fei-Fei, L.6
-
31
-
-
85058215742
-
Fast parameter adaptation for few-shot image captioning visual question answering
-
X. Dong, L. Zhu, D. Zhang, Y. Yang, and F. Wu. 2018. Fast parameter adaptation for few-shot image captioning and visual question answering. In Proceedings of the ACM International Conference on Multimedia. 54-62.
-
(2018)
Proceedings of the Acm International Conference on Multimedia
, pp. 54-62
-
-
Dong, X.1
Zhu, L.2
Zhang, D.3
Yang, Y.4
Wu, F.5
-
33
-
-
85047012880
-
One-shot imitation learning
-
MIT Press
-
Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba. 2017. One-shot imitation learning. In Advances in Neural Information Processing Systems. MIT Press, 1087-1098.
-
(2017)
Advances in Neural Information Processing Systems
, pp. 1087-1098
-
-
Duan, Y.1
Andrychowicz, M.2
Stadie, B.3
Ho, J.4
Schneider, J.5
Sutskever, I.6
Abbeel, P.7
Zaremba, W.8
-
35
-
-
33144466753
-
One-shot learning of object categories
-
2006
-
L. Fei-Fei, R. Fergus, and P. Perona. 2006. One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28, 4 (2006), 594-611.
-
(2006)
Ieee Trans. Pattern Anal. Mach. Intell
, vol.28
, Issue.4
, pp. 594-611
-
-
Fei-Fei, L.1
Fergus, R.2
Perona, P.3
-
36
-
-
84898963788
-
Object classification from a single example utilizing class relevance metrics
-
MIT Press
-
M. Fink. 2005. Object classification from a single example utilizing class relevance metrics. In Advances in Neural Information Processing Systems. MIT Press, 449-456.
-
(2005)
Advances in Neural Information Processing Systems
, pp. 449-456
-
-
Fink, M.1
-
40
-
-
85057302642
-
Bilevel programming for hyperparameter optimization and meta-learning
-
L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, and M. Pontil. 2018. Bilevel programming for hyperparameter optimization and meta-learning. In Proceedings of the International Conference on Machine Learning. 1563-1572.
-
(2018)
Proceedings of the International Conference on Machine Learning
, pp. 1563-1572
-
-
Franceschi, L.1
Frasconi, P.2
Salzo, S.3
Grazzi, R.4
Pontil, M.5
-
42
-
-
85058572039
-
Low-shot learning via covariance-preserving adversarial augmentation networks
-
MIT Press
-
H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang. 2018. Low-shot learning via covariance-preserving adversarial augmentation networks. In Advances in Neural Information Processing Systems. MIT Press, 983-993.
-
(2018)
Advances in Neural Information Processing Systems
, pp. 983-993
-
-
Gao, H.1
Shou, Z.2
Zareian, A.3
Zhang, H.4
Chang, S.5
-
46
-
-
84937849144
-
Generative adversarial nets
-
MIT Press
-
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. MIT Press, 2672-2680.
-
(2014)
Advances in Neural Information Processing Systems
, pp. 2672-2680
-
-
Goodfellow, I.1
Pouget-Abadie, J.2
Mirza, M.3
Xu, B.4
Warde-Farley, D.5
Ozair, S.6
Courville, A.7
Bengio, Y.8
-
51
-
-
84977536783
-
Learning assistive strategies from a few userrobot interactions: Model-based reinforcement learning approach
-
M. Hamaya, T. Matsubara, T. Noda, T. Teramae, and J. Morimoto. 2016. Learning assistive strategies from a few userrobot interactions: Model-based reinforcement learning approach. In Proceedings of the International Conference on Robotics and Automation. 3346-3351.
-
(2016)
Proceedings of the International Conference on Robotics and Automation
, pp. 3346-3351
-
-
Hamaya, M.1
Matsubara, T.2
Noda, T.3
Teramae, T.4
Morimoto, J.5
-
52
-
-
85081734437
-
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-The-art evaluation
-
X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun. 2018. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-The-art evaluation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 4803-4809.
-
(2018)
Proceedings of the Conference on Empirical Methods in Natural Language Processing
, pp. 4803-4809
-
-
Han, X.1
Zhu, H.2
Yu, P.3
Wang, Z.4
Yao, Y.5
Liu, Z.6
Sun, M.7
-
57
-
-
85059386002
-
The variational homoencoder: Learning to learn high capacity generative models from few examples
-
Elsevier/North Holland
-
L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum. 2018. The variational homoencoder: Learning to learn high capacity generative models from few examples. In Uncertainty in Artificial Intelligence. Elsevier/North Holland, 988-997.
-
(2018)
Uncertainty in Artificial Intelligence
, pp. 988-997
-
-
Hewitt, L.B.1
Nye, M.I.2
Gane, A.3
Jaakkola, T.4
Tenenbaum, J.B.5
-
58
-
-
0031573117
-
Long short-term memory
-
1997
-
S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735-1780.
-
(1997)
Neural Comput.
, vol.9
, Issue.8
, pp. 1735-1780
-
-
Hochreiter, S.1
Schmidhuber, J.2
-
60
-
-
85071504647
-
One-shot adaptation of supervised deep convolutional models
-
J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and T. Darrell. 2013. One-shot adaptation of supervised deep convolutional models. In Proceedings of the International Conference on Learning Representations.
-
(2013)
Proceedings of the International Conference on Learning Representations
-
-
Hoffman, J.1
Tzeng, E.2
Donahue, J.3
Jia, Y.4
Saenko, K.5
Darrell, T.6
-
61
-
-
85119448952
-
Few-shot charge prediction with discriminative legal attributes
-
Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun. 2018. Few-shot charge prediction with discriminative legal attributes. In Proceedings of the International Conference on Computational Linguistics. 487-498.
-
(2018)
Proceedings of the International Conference on Computational Linguistics
, pp. 487-498
-
-
Hu, Z.1
Li, X.2
Tu, C.3
Liu, Z.4
Sun, M.5
-
63
-
-
84913580146
-
Caffe: Convolutional architecture for fast feature embedding
-
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia. 675-678.
-
(2014)
Proceedings of the Acm International Conference on Multimedia
, pp. 675-678
-
-
Jia, Y.1
Shelhamer, E.2
Donahue, J.3
Karayev, S.4
Long, J.5
Girshick, R.6
Guadarrama, S.7
Darrell, T.8
-
69
-
-
85016395012
-
Overcoming catastrophic forgetting in neural networks
-
2017
-
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al. 2017. Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. U.S.A. 114, 13 (2017), 3521-3526.
-
(2017)
Proc. Natl. Acad. Sci. U.S.A.
, vol.114
, Issue.13
, pp. 3521-3526
-
-
Kirkpatrick, J.1
Pascanu, R.2
Rabinowitz, N.3
Veness, J.4
Desjardins, G.5
Rusu, A.A.6
Milan, K.7
Quan, J.8
Ramalho, T.9
Grabska-Barwinska, A.10
-
71
-
-
85020694807
-
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
-
2017
-
L. Kotthoff, C. Thornton, H. H. Hoos, F. Hutter, and K. Leyton-Brown. 2017. Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. 18, 1 (2017), 826-830.
-
(2017)
J. Mach. Learn. Res.
, vol.18
, Issue.1
, pp. 826-830
-
-
Kotthoff, L.1
Thornton, C.2
Hoos, H.H.3
Hutter, F.4
Leyton-Brown, K.5
-
76
-
-
84949683101
-
Human-level concept learning through probabilistic program induction
-
2015
-
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. 2015. Human-level concept learning through probabilistic program induction. Science 350, 6266 (2015), 1332-1338.
-
(2015)
Science
, vol.350
, Issue.6266
, pp. 1332-1338
-
-
Lake, B.M.1
Salakhutdinov, R.2
Tenenbaum, J.B.3
-
82
-
-
85058102640
-
Feature space transfer for data augmentation
-
B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos. 2018. Feature space transfer for data augmentation. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 9090-9098.
-
(2018)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 9090-9098
-
-
Liu, B.1
Wang, X.2
Dixit, M.3
Kwitt, R.4
Vasconcelos, N.5
-
84
-
-
85083950649
-
Learning to propopagate labels: Transductive propagation network for few-shot learning
-
Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. Hwang, and Y Yang. 2019. Learning to propopagate labels: Transductive propagation network for few-shot learning. In Proceedings of the International Conference on Learning Representations.
-
(2019)
Proceedings of the International Conference on Learning Representations
-
-
Liu, Y.1
Lee, J.2
Park, M.3
Kim, S.4
Yang, E.5
Hwang, S.6
Yang, Y.7
-
86
-
-
0028531585
-
Quantifying prior determination knowledge using the PAC learning model
-
1994
-
S. Mahadevan and P. Tadepalli. 1994. Quantifying prior determination knowledge using the PAC learning model. Mach. Learn. 17, 1 (1994), 69-105.
-
(1994)
Mach. Learn.
, vol.17
, Issue.1
, pp. 69-105
-
-
Mahadevan, S.1
Tadepalli, P.2
-
89
-
-
85072822252
-
Key-value memory networks for directly reading documents
-
A. Miller, A. Fisch, J. Dodge, A.-H. Karimi, A. Bordes, and J.Weston. 2016. Key-value memory networks for directly reading documents. In Proceedings of the Conference on EmpiricalMethods in Natural Language Processing. 1400-1409.
-
(2016)
Proceedings of the Conference on EmpiricalMethods in Natural Language Processing
, pp. 1400-1409
-
-
Miller, A.1
Fisch, A.2
Dodge, J.3
Karimi, A.-H.4
Bordes, A.5
Weston, J.6
-
101
-
-
77956031473
-
A survey on transfer learning
-
2010
-
S. J. Pan and Q. Yang. 2010. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 10, 22 (2010), 1345-1359.
-
(2010)
Ieee Trans. Knowl. Data Eng.
, vol.10
, Issue.22
, pp. 1345-1359
-
-
Pan, S.J.1
Yang, Q.2
-
107
-
-
85083951938
-
Fewshot autoregressive density estimation: Towards learning to learn distributions
-
S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas. 2018. Fewshot autoregressive density estimation: Towards learning to learn distributions. In Proceedings of the International Conference on Learning Representations.
-
(2018)
Proceedings of the International Conference on Learning Representations
-
-
Reed, S.1
Chen, Y.2
Paine, T.3
Van Den Oord, A.4
Eslami, S.M.A.5
Rezende, D.6
Vinyals, O.7
De Freitas, N.8
-
108
-
-
85083952964
-
Metalearning for semi-supervised few-shot classification
-
M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel. 2018. Metalearning for semi-supervised few-shot classification. In Proceedings of the International Conference on Learning Representations.
-
(2018)
Proceedings of the International Conference on Learning Representations
-
-
Ren, M.1
Ravi, S.2
Triantafillou, E.3
Snell, J.4
Swersky, K.5
Tenenbaum, J.B.6
Larochelle, H.7
Zemel, R.S.8
-
111
-
-
85083951643
-
Meta-learning with latent embedding optimization
-
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell. 2019. Meta-learning with latent embedding optimization. In Proceedings of the International Conference on Learning Representations.
-
(2019)
Proceedings of the International Conference on Learning Representations
-
-
Rusu, A.A.1
Rao, D.2
Sygnowski, J.3
Vinyals, O.4
Pascanu, R.5
Osindero, S.6
Hadsell, R.7
-
114
-
-
84998717754
-
Meta-learning with memory-augmented neural networks
-
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In Proceedings of the International Conference on Machine Learning. 1842-1850.
-
(2016)
Proceedings of the International Conference on Machine Learning
, pp. 1842-1850
-
-
Santoro, A.1
Bartunov, S.2
Botvinick, M.3
Wierstra, D.4
Lillicrap, T.5
-
116
-
-
85069796838
-
Deltaencoder: An effective sample synthesis method for few-shot object recognition
-
MIT Press
-
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein. 2018. Deltaencoder: An effective sample synthesis method for few-shot object recognition. In Advances in Neural Information Processing Systems. MIT Press, 2850-2860.
-
(2018)
Advances in Neural Information Processing Systems
, pp. 2850-2860
-
-
Schwartz, E.1
Karlinsky, L.2
Shtok, J.3
Harary, S.4
Marder, M.5
Kumar, A.6
Feris, R.7
Giryes, R.8
Bronstein, A.9
-
117
-
-
68949137209
-
-
Technical Report. University of Wisconsin-Madison Department of Computer Sciences
-
B. Settles. 2009. Active Learning Literature Survey. Technical Report. University of Wisconsin-Madison Department of Computer Sciences.
-
(2009)
Active Learning Literature Survey
-
-
Settles, B.1
-
120
-
-
84963949906
-
Mastering the game of Go with deep neural networks and tree search
-
2016
-
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484-489.
-
(2016)
Nature
, vol.529
, Issue.7587
, pp. 484-489
-
-
Silver, D.1
Huang, A.2
Maddison, C.J.3
Guez, A.4
Sifre, L.5
Van Den Driessche, G.6
Schrittwieser, J.7
Antonoglou, I.8
Panneershelvam, V.9
Lanctot, M.10
-
126
-
-
85061641334
-
Learning to compare: Relation network for few-shot learning
-
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales. 2018. Learning to compare: Relation network for few-shot learning. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 1199-1208.
-
(2018)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 1199-1208
-
-
Sung, F.1
Yang, Y.2
Zhang, L.3
Xiang, T.4
Torr, P.H.5
Hospedales, T.M.6
-
131
-
-
85074526522
-
-
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P.-A. Manzagol, et al. 2019. Meta-dataset: A dataset of datasets for learning to learn from few examples. arXiv preprint arXiv:1903.03096.
-
(2019)
Meta-dataset: A Dataset of Datasets for Learning to Learn from Few Examples
-
-
Triantafillou, E.1
Zhu, T.2
Dumoulin, V.3
Lamblin, P.4
Xu, K.5
Goroshin, R.6
Gelada, C.7
Swersky, K.8
Manzagol, P.-A.9
-
134
-
-
0002988210
-
Computing machinery and intelligence
-
1950
-
M. A. Turing. 1950. Computing machinery and intelligence. Mind 59, 236 (1950), 433-433.
-
(1950)
Mind
, vol.59
, Issue.236
, pp. 433-433
-
-
Turing, M.A.1
-
135
-
-
85018873682
-
Conditional image generation with PixelCNN decoders
-
MIT Press
-
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, et al. 2016. Conditional image generation with PixelCNN decoders. In Advances in Neural Information Processing Systems. MIT Press, 4790-4798.
-
(2016)
Advances in Neural Information Processing Systems
, pp. 4790-4798
-
-
Van Den Oord, A.1
Kalchbrenner, N.2
Espeholt, L.3
Vinyals, O.4
Graves, A.5
-
137
-
-
85046997062
-
Ameta-learning perspective on cold-start recommendations for items
-
MIT Press
-
M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle. 2017.Ameta-learning perspective on cold-start recommendations for items. In Advances in Neural Information Processing Systems. MIT Press, 6904-6914.
-
(2017)
Advances in Neural Information Processing Systems
, pp. 6904-6914
-
-
Vartak, M.1
Thiagarajan, A.2
Miranda, C.3
Bratman, J.4
Larochelle, H.5
-
138
-
-
85018863845
-
Matching networks for one shot learning
-
MIT Press
-
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al. 2016. Matching networks for one shot learning. In Advances in Neural Information Processing Systems. MIT Press, 3630-3638.
-
(2016)
Advances in Neural Information Processing Systems
, pp. 3630-3638
-
-
Vinyals, O.1
Blundell, C.2
Lillicrap, T.3
Wierstra, D.4
-
139
-
-
85030992797
-
Multi-attention network for one shot learning
-
P.Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, and H. Tao Shen. 2017. Multi-attention network for one shot learning. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 2721-2729.
-
(2017)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 2721-2729
-
-
Wang, P.1
Liu, L.2
Shen, C.3
Huang, Z.4
Van Den Hengel, A.5
Shen, H.T.6
-
142
-
-
85018930461
-
Learning from small sample sets by combining unsupervised meta-training with CNNs
-
MIT Press
-
Y.-X. Wang and M. Hebert. 2016. Learning from small sample sets by combining unsupervised meta-training with CNNs. In Advances in Neural Information Processing Systems. MIT Press, 244-252.
-
(2016)
Advances in Neural Information Processing Systems
, pp. 244-252
-
-
Wang, Y.-X.1
Hebert, M.2
-
148
-
-
85058224842
-
Exploit the unknown gradually: One-shot videobased person re-identification by stepwise learning
-
Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang. 2018. Exploit the unknown gradually: One-shot videobased person re-identification by stepwise learning. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 5177-5186.
-
(2018)
Proceedings of the Conference on Computer Vision and Pattern Recognition
, pp. 5177-5186
-
-
Wu, Y.1
Lin, Y.2
Dong, X.3
Yan, Y.4
Ouyang, W.5
Yang, Y.6
-
150
-
-
85042208215
-
Few-shot learning for short text classification
-
2018
-
L. Yan, Y. Zheng, and J. Cao. 2018. Few-shot learning for short text classification. Multimedia Tools Appl. 77, 22 (2018), 29799-29810.
-
(2018)
Multimedia Tools Appl.
, vol.77
, Issue.22
, pp. 29799-29810
-
-
Yan, L.1
Zheng, Y.2
Cao, J.3
-
153
-
-
85066352605
-
-
Q. Yao, M. Wang, E. H. Jair, I. Guyon, Y.-Q. Hu, Y.-F. Li, W.-W. Tu, Q. Yang, and Y. Yu. 2018. Taking human out of learning applications: A survey on automated machine learning. arXiv preprint arXiv:1810.13306.
-
(2018)
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
-
-
Yao, Q.1
Wang, M.2
Jair, E.H.3
Guyon, I.4
Hu, Y.-Q.5
Li, Y.-F.6
Tu, W.-W.7
Yang, Q.8
Yu, Y.9
-
156
-
-
85064441999
-
Bayesian model-agnostic meta-learning
-
MIT Press
-
J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn. 2018. Bayesian model-agnostic meta-learning. In Advances in Neural Information Processing Systems. MIT Press, 7343-7353.
-
(2018)
Advances in Neural Information Processing Systems
, pp. 7343-7353
-
-
Yoon, J.1
Kim, T.2
Dia, O.3
Kim, S.4
Bengio, Y.5
Ahn, S.6
-
157
-
-
85064455066
-
Diverse few-shot text classification with multiple metrics
-
M. Yu, X. Guo, J. Yi, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, and B. Zhou. 2018. Diverse few-shot text classification with multiple metrics. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1206-1215.
-
(2018)
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
, pp. 1206-1215
-
-
Yu, M.1
Guo, X.2
Yi, J.3
Chang, S.4
Potdar, S.5
Cheng, Y.6
Tesauro, G.7
Wang, H.8
Zhou, B.9
-
158
-
-
85059288228
-
Advances in variational inference
-
2019
-
C. Zhang, J. Butepage, H. Kjellstrom, and S. Mandt. 2019. Advances in variational inference. IEEE Trans. Pattern Anal. Mach. Intell. 41, 8 (2019), 2008-2026.
-
(2019)
Ieee Trans. Pattern Anal. Mach. Intell.
, vol.41
, Issue.8
, pp. 2008-2026
-
-
Zhang, C.1
Butepage, J.2
Kjellstrom, H.3
Mandt, S.4
-
159
-
-
85064803785
-
MetaGAN: An adversarial approach to few-shot learning
-
MIT Press
-
R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song. 2018. MetaGAN: An adversarial approach to few-shot learning. In Advances in Neural Information Processing Systems. MIT Press, 2371-2380.
-
(2018)
Advances in Neural Information Processing Systems
, pp. 2371-2380
-
-
Zhang, R.1
Che, T.2
Ghahramani, Z.3
Bengio, Y.4
Song, Y.5
-
160
-
-
85074222465
-
Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data
-
Y. Zhang, H. Tang, and K. Jia. 2018. Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data. In Proceedings of the European Conference on Computer Vision. 233-248.
-
(2018)
Proceedings of the European Conference on Computer Vision
, pp. 233-248
-
-
Zhang, Y.1
Tang, H.2
Jia, K.3
-
163
-
-
85042566177
-
A brief introduction to weakly supervised learning
-
2017
-
Z.-H. Zhou. 2017. A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5, 1 (2017), 44-53.
-
(2017)
Natl. Sci. Rev.
, vol.5
, Issue.1
, pp. 44-53
-
-
Zhou, Z.-H.1
-
165
-
-
33745456231
-
-
Technical Report. University of Wisconsin-Madison Department of Computer Sciences
-
X. J. Zhu. 2005. Semi-supervised Learning Literature Survey. Technical Report. University of Wisconsin-Madison Department of Computer Sciences.
-
(2005)
Semi-supervised Learning Literature Survey
-
-
Zhu, X.J.1
|