Fetching the paper…
Reading the bibliography…
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small.
Computing machinery and intelligence
M. A. Turing. 1950 · 1950
Earlier work this paper cites.
A Comprehensive Introduction to Differential Geometry
M. D. Spivak. 1970 · 1970
Earlier work this paper cites.
Principles of risk minimization for learning theory. In Advances in Neural Information Processing Systems . 831–838
V. N. Vapnik. 1992 · 1992
Earlier work this paper cites.
Signature verification using a "siamese" time delay neural network. In Advances in Neural Information Processing Systems . 737–744
J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah. 1994 · 1994
Earlier work this paper cites.
Quantifying prior determination knowledge using the PAC learning model
S. Mahadevan and P. Tadepalli. 1994 · 1994
Earlier work this paper cites.
Multitask learning
R. Caruana. 1997 · 1997
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Machine Learning
M. T. Mitchell. 1997 · 1997
Earlier work this paper cites.
Learning from one example through shared densities on transforms. In Conference on Computer Vision and Pattern Recognition , Vol. 1. 464–471
E. G. Miller, N. E. Matsakis, and P. A. Viola. 2000 · 2000
Earlier work this paper cites.
The Elements of Statistical Learning . Vol. 1
J. Friedman, T. Hastie, and R. Tibshirani. 2001 · 2001
Earlier work this paper cites.
Learning to learn using gradient descent. In International Conference on Artificial Neural Networks . 87–94
S. Hochreiter, A. S. Younger, and P. R. Conwell. 2001 · 2001
Earlier work this paper cites.
Cross-generalization: Learning novel classes from a single example by feature replacement. In Conference on Computer Vision and Pattern Recognition , Vol. 1. 672–679
E. Bart and S. Ullman. 2005 · 2005
Earlier work this paper cites.
Object classification from a single example utilizing class relevance metrics. In Advances in Neural Information Processing Systems . 449–456
M. Fink. 2005 · 2005
Earlier work this paper cites.
Semi-supervised learning literature survey
X. J. Zhu. 2005 · 2005
Earlier work this paper cites.
Pattern Recognition and Machine Learning
C. M. Bishop. 2006 · 2006
Earlier work this paper cites.
One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona. 2006 · 2006
Earlier work this paper cites.
Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems . 137–144
S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. 2007 · 2007
Earlier work this paper cites.
Learning bounds for domain adaptation. In Advances in Neural Information Processing Systems . 129–136
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman. 2008 · 2008
Earlier work this paper cites.
The tradeoffs of large scale learning. In Advances in Neural Information Processing Systems . 161–168
L. Bottou and O. Bousquet. 2008 · 2008
Earlier work this paper cites.
Learning from imbalanced data
H. He and E. A. Garcia. 2008 · 2008
Earlier work this paper cites.
Introduction to Robotics: Mechanics and Control
J. J. Craig. 2009 · 2009
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database. In Conference on Computer Vision and Pattern Recognition . 248–255
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer. In Conference on Computer Vision and Pattern Recognition . 951–958
C. H. Lampert, H. Nickisch, and S. Harmeling. 2009 · 2009
Earlier work this paper cites.
Positive unlabeled learning for data stream classification. In SIAM International Conference on Data Mining . 259–270
X.-L. Li, P. S. Yu, B. Liu, and S.-K. Ng. 2009 · 2009
Earlier work this paper cites.
Deep boltzmann machines. In International Conference on Artificial Intelligence and Statistics . 448–455
R. Salakhutdinov and G. Hinton. 2009 · 2009
Earlier work this paper cites.
Active learning literature survey
B. Settles. 2009 · 2009
Earlier work this paper cites.
A survey on transfer learning
S. J. Pan and Q. Yang. 2010 · 2010
Earlier work this paper cites.
Optimizing one-shot recognition with micro-set learning. In Conference on Computer Vision and Pattern Recognition . 3027–3034
K. D. Tang, M. F. Tappen, R. Sukthankar, and C. H. Lampert. 2010 · 2010
Earlier work this paper cites.
Towards one shot learning by imitation for humanoid robots. In International Conference on Robotics and Automation . 2889–2894
Y. Wu and Y. Demiris. 2010 · 2010
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems . 1097–1105
A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012 · 2012
Earlier work this paper cites.
One-shot learning with a hierarchical nonparametric Bayesian model. In ICML Workshop on Unsupervised and Transfer Learning . 195–206
R. Salakhutdinov, J. Tenenbaum, and A. Torralba. 2012 · 2012
Earlier work this paper cites.
Learning manipulation actions from a few demonstrations. In International Conference on Robotics and Automation . 1268–1275
N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss. 2013 · 2013
Earlier work this paper cites.
Label-embedding for attribute-based classification. In Conference on Computer Vision and Pattern Recognition . 819–826
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. 2013 · 2013
Earlier work this paper cites.
One-shot adaptation of supervised deep convolutional models. In International Conference on Learning Representations
J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and T. Darrell. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets. In Advances in Neural Information Processing Systems . 2672–2680
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014 · 2014
Earlier work this paper cites.
A. Graves, G. Wayne, and I. Danihelka. 2014 · 2014
Earlier work this paper cites.
A unified semantic embedding: Relating taxonomies and attributes. In Advances in Neural Information Processing Systems . 271–279
S. J. Hwang and L. Sigal. 2014 · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding. In ACM International Conference on Multimedia . 675–678
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. 2014 · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes. In International Conference on Learning Representations
D. P. Kingma and M. Welling. 2014 · 2014
Earlier work this paper cites.
One-shot learning of generative speech concepts. In Annual Meeting of the Cognitive Science Society , Vol. 36
B. Lake, C.-Y. Lee, J. Glass, and J. Tenenbaum. 2014 · 2014
Earlier work this paper cites.
Costa: Co-occurrence statistics for zero-shot classification. In Conference on Computer Vision and Pattern Recognition . 2441–2448
T. Mensink, E. Gavves, and C. Snoek. 2014 · 2014
Earlier work this paper cites.
Domain-adaptive discriminative one-shot learning of gestures. In European Conference on Computer Vision . 814–829
T. Pfister, J. Charles, and A. Zisserman. 2014 · 2014
Earlier work this paper cites.
J. Weston, S. Chopra, and A. Bordes. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations
Bengio Y. Bahdanau D, Cho K. 2015 · 2015
Earlier work this paper cites.
Deep feature synthesis: Towards automating data science endeavors. In International Conference on Data Science and Advanced Analytics . 1–10
J. M. Kanter and K. Veeramachaneni. 2015 · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
G. Koch. 2015 · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. 2015 · 2015
Earlier work this paper cites.
Training very deep networks. In Advances in Neural Information Processing Systems . 2377–2385
R. K. Srivastava, K. Greff, and J. Schmidhuber. 2015 · 2015
Earlier work this paper cites.
End-to-end memory networks. In Advances in Neural Information Processing Systems . 2440–2448
S. Sukhbaatar, J. Weston, R. Fergus, et al · 2015
Earlier work this paper cites.
Multi-task transfer methods to improve one-shot learning for multimedia event detection. In British Machine Vision Conference
W. Yan, J. Yap, and G. Mori. 2015 · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent. In Advances in Neural Information Processing Systems . 3981–3989
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas. 2016 · 2016
Earlier work this paper cites.
Learning feed-forward one-shot learners. In Advances in Neural Information Processing Systems . 523–531
L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi. 2016 · 2016
Earlier work this paper cites.
PAC-Bayesian theory meets Bayesian inference. In Advances in Neural Information Processing Systems . 1884–1892
P. Germain, F. Bach, A. Lacoste, and S. Lacoste-Julien. 2016 · 2016
Earlier work this paper cites.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville. 2016 · 2016
Cited alongside, same era.
Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach. In International Conference on Robotics and Automation . 3346–3351
M. Hamaya, T. Matsubara, T. Noda, T. Teramae, and J. Morimoto. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition. In Conference on Computer Vision and Pattern Recognition . 770–778
K. He, X. Zhang, S. Ren, and J. Sun. 2016 · 2016
Cited alongside, same era.
One-shot learning of scene locations via feature trajectory transfer. In Conference on Computer Vision and Pattern Recognition . 78–86
R. Kwitt, S. Hegenbart, and M. Niethammer. 2016 · 2016
Cited alongside, same era.
Key-value memory networks for directly reading documents. In Conference on Empirical Methods in Natural Language Processing . 1400–1409
A. Miller, A. Fisch, J. Dodge, A.-H. Karimi, A. Bordes, and J. Weston. 2016 · 2016
Probabilistic model-agnostic meta-learning. In Advances in Neural Information Processing Systems . 9537–9548
C. Finn, K. Xu, and S. Levine. 2018 · 2018
Later among the works it cites.
Bilevel programming for hyperparameter optimization and meta-learning. In International Conference on Machine Learning . 1563–1572
L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, and M. Pontil. 2018 · 2018
Later among the works it cites.
Low-shot learning via covariance-preserving adversarial augmentation networks. In Advances in Neural Information Processing Systems . 983–993
H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang. 2018 · 2018
Later among the works it cites.
Dynamic few-shot visual learning without forgetting. In Conference on Computer Vision and Pattern Recognition . 4367–4375
S. Gidaris and N. Komodakis. 2018 · 2018
Later among the works it cites.
Recasting gradient-based meta-learning as hierarchical Bayes. In International Conference on Learning Representations
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths. 2018 · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
One-shot generalization in deep generative models. In International Conference on Machine Learning . 1521–1529
D. Rezende, I. Danihelka, K. Gregor, and D. Wierstra. 2016 · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks. In International Conference on Machine Learning . 1842–1850
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. 2016 · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
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
Cited alongside, same era.
Conditional image generation with PixelCNN decoders. In Advances in Neural Information Processing Systems . 4790–4798
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, et al · 2016
Cited alongside, same era.
Matching networks for one shot learning. In Advances in Neural Information Processing Systems . 3630–3638
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
Cited alongside, same era.
Low data drug discovery with one-shot learning
H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. 2017 · 2017
Cited alongside, same era.
Learning algorithms for active learning. In International Conference on Machine Learning . 301–310
P. Bachman, A. Sordoni, and A. Trischler. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Few-shot human motion prediction via meta-learning. In European Conference on Computer Vision . 432–450
L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. Moura. 2018 · 2018
Later among the works it cites.
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In Conference on Empirical Methods in Natural Language Processing . 4803–4809
X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun. 2018 · 2018
Later among the works it cites.
The variational homoencoder: Learning to learn high capacity generative models from few examples. In Uncertainty in Artificial Intelligence . 988–997
L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum. 2018 · 2018
Later among the works it cites.
Few-shot charge prediction with discriminative legal attributes. In International Conference on Computational Linguistics . 487–498
Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun. 2018 · 2018
Later among the works it cites.
Extending a parser to distant domains using a few dozen partially annotated examples. In Annual Meeting of the Association for Computational Linguistics . 1190–1199
V. Joshi, M. Peters, and M. Hopkins. 2018 · 2018
Later among the works it cites.
Learning structure and strength of CNN filters for small sample size training. In Conference on Computer Vision and Pattern Recognition . 9349–9358
R. Keshari, M. Vatsa, R. Singh, and A. Noore. 2018 · 2018
Later among the works it cites.
CLEAR: Cumulative learning for one-shot one-class image recognition. In Conference on Computer Vision and Pattern Recognition . 3446–3455
J. Kozerawski and M. Turk. 2018 · 2018
Later among the works it cites.
Gradient-based meta-learning with learned layerwise metric and subspace. In International Conference on Machine Learning . 2933–2942
Y. Lee and S. Choi. 2018 · 2018
Later among the works it cites.
Feature space transfer for data augmentation. In Conference on Computer Vision and Pattern Recognition . 9090–9098
B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos. 2018 · 2018
Later among the works it cites.
A simple neural attentive meta-learner. In International Conference on Learning Representations
N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel. 2018 · 2018
Later among the works it cites.
Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion. In INTERSPEECH . 2833–2837
S. H. Mohammadi and T. Kim. 2018 · 2018
Later among the works it cites.
Foundations of machine learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar. 2018 · 2018
Later among the works it cites.
Rapid adaptation with conditionally shifted neurons. In International Conference on Machine Learning . 3661–3670
T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler. 2018 · 2018
Later among the works it cites.
Deep online learning via meta-learning: Continual adaptation for model-based RL. In International Conference on Learning Representations
A. Nagabandi, C. Finn, and S. Levine. 2018 · 2018
Later among the works it cites.
Improved algorithms for collaborative PAC learning. In Advances in Neural Information Processing Systems . 7631–7639
H. Nguyen and L. Zakynthinou. 2018 · 2018
Later among the works it cites.
TADAM: Task dependent adaptive metric for improved few-shot learning. In Advances in Neural Information Processing Systems . 719–729
B. Oreshkin, P. R. López, and A. Lacoste. 2018 · 2018
Later among the works it cites.
Low-shot learning with imprinted weights. In Conference on Computer Vision and Pattern Recognition . 5822–5830
H. Qi, M. Brown, and D. G. Lowe. 2018 · 2018
Later among the works it cites.
Few-shot autoregressive density estimation: Towards learning to learn distributions. In International Conference on Learning Representations
S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas. 2018 · 2018
Later among the works it cites.
Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations
M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel. 2018 · 2018
Later among the works it cites.
Few-shot and zero-shot multi-label learning for structured label spaces. In Conference on Empirical Methods in Natural Language Processing . 3132
A. Rios and R. Kavuluru. 2018 · 2018
Later among the works it cites.
Few-shot learning with graph neural networks. In International Conference on Learning Representations
V. G. Satorras and J. B. Estrach. 2018 · 2018
Later among the works it cites.
Delta-encoder: An effective sample synthesis method for few-shot object recognition. In Advances in Neural Information Processing Systems . 2850–2860
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein. 2018 · 2018
Later among the works it cites.
Small sample learning in big data era
J. Shu, Z. Xu, and D Meng. 2018 · 2018
Later among the works it cites.
Memory, show the way: Memory based few shot word representation learning. In Conference on Empirical Methods in Natural Language Processing . 1435–1444
J. Sun, S. Wang, and C. Zong. 2018 · 2018
Later among the works it cites.
Learning to compare: Relation network for few-shot learning. In Conference on Computer Vision and Pattern Recognition . 1199–1208
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales. 2018 · 2018
Later among the works it cites.
Machine speech chain with one-shot speaker adaptation. In INTERSPEECH . 887–891
A. Tjandra, S. Sakti, and S. Nakamura. 2018 · 2018
Later among the works it cites.
Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning. In Conference on Computer Vision and Pattern Recognition . 5177–5186
Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang. 2018 · 2018
Later among the works it cites.
Few-shot learning for short text classification
L. Yan, Y. Zheng, and J. Cao. 2018 · 2018
Later among the works it cites.
One-shot action localization by learning sequence matching network. In Conference on Computer Vision and Pattern Recognition . 1450–1459
H. Yang, X. He, and F. Porikli. 2018 · 2018
Later among the works it cites.
Taking human out of learning applications: A survey on automated machine learning
Q. Yao, M. Wang, E. H. Jair, I. Guyon, Y.-Q. Hu, Y.-F. Li, W.-W. Tu, Q. Yang, and Y. Yu. 2018 · 2018
Later among the works it cites.
Efficient k-shot learning with regularized deep networks. In AAAI Conference on Artificial Intelligence
D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani. 2018 · 2018
Later among the works it cites.
Bayesian model-agnostic meta-learning. In Advances in Neural Information Processing Systems . 7343–7353
J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn. 2018 · 2018
Later among the works it cites.
Diverse few-shot text classification with multiple metrics. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1206–1215
M. Yu, X. Guo, J. Yi, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, and B. Zhou. 2018 · 2018
Later among the works it cites.
Dynamic conditional networks for few-shot learning. In European Conference on Computer Vision
F. Zhao, J. Zhao, S. Yan, and J. Feng. 2018 · 2018
Later among the works it cites.
Compound memory networks for few-shot video classification. In European Conference on Computer Vision . 751–766
L. Zhu and Y. Yang. 2018 · 2018
Later among the works it cites.
Meta-learning with differentiable closed-form solvers. In International Conference on Learning Representations
L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi. 2019 · 2019
Closest in time.
Meta-learning language-guided policy learning. In International Conference on Learning Representations
J. D. Co-Reyes, A. Gupta, S. Sanjeev, N. Altieri, J. DeNero, P. Abbeel, and S. Levine. 2019 · 2019
Closest in time.
AutoAugment: Learning augmentation policies from data. In Conference on Computer Vision and Pattern Recognition . 113–123
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. 2019 · 2019
Closest in time.
Meta-learning probabilistic inference for prediction. In International Conference on Learning Representations
J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner. 2019 · 2019
Closest in time.
Adaptive posterior learning: Few-shot learning with a surprise-based memory module. In International Conference on Learning Representations
T. Ramalho and M. Garnelo. 2019 · 2019
Closest in time.
Amortized Bayesian meta-learning. In International Conference on Learning Representations
S. Ravi and A. Beatson. 2019 · 2019
Closest in time.
Meta-learning with latent embedding optimization. In International Conference on Learning Representations
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell. 2019 · 2019
Closest in time.
Meta-dataset: A dataset of datasets for learning to learn from few examples
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P.-A. Manzagol, et al · 2019
Closest in time.
EDA: Easy data augmentation techniques for boosting performance on text classification tasks. In Conference on Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing . 6383–6389
J. Wei and K. Zou. 2019 · 2019
Closest in time.
Efficient neural architecture search via proximal iterations. In AAAI Conference on Artificial Intelligence
Q. Yao, J. Xu, W.-W. Tu, and Z. Zhu. 2020 · 2020
Closest in time.
Advances in variational inference
C. Zhang, J. Butepage, H. Kjellstrom, and S. Mandt. 2019 · 2026
Closest in time.
Learning to learn with compound HD models. In Advances in Neural Information Processing Systems . 2061–2069
A. Torralba, J. B. Tenenbaum, and R. R. Salakhutdinov. 2011 · 2069
Closest in time.