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Zero-shot learning is the problem of predicting instances over classes not seen during training.
Explanation-based learning: An alternative view
DeJong, G. and Mooney, R · 1986
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Explanation-based generalization: A unifying view
Mitchell, T. M., Keller, R. M., and Kedar-Cabelli, S. T · 1986
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Newsweeder: Learning to filter netnews
Lang, K · 1995
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Wordnet: a lexical database for english
Miller, G. A · 1995
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Rcv1: A new benchmark collection for text categorization research
Lewis, D. D., Yang, Y., Russell-Rose, T., and Li, F · 2004
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Importance of semantic representation: Dataless classification
Chang, M.-W., Ratinov, L.-A., Roth, D., and Srikumar, V · 2008
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Zero-data learning of new tasks
Larochelle, H., Erhan, D., and Bengio, Y · 2008
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80 million tiny images: A large data set for nonparametric object and scene recognition
Torralba, A., Fergus, R., and Freeman, W. T · 2008
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Supervised semantic indexing
Bai, B., Weston, J., Grangier, D., Collobert, R., Sadamasa, K., Qi, Y., Chapelle, O., and Weinberger, K · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S · 2009
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Zero-shot learning with semantic output codes
Palatucci, M., Pomerleau, D., Hinton, G. E., and Mitchell, T. M · 2009
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Reading between the lines: Learning to map high-level instructions to commands
Branavan, S., Zettlemoyer, L., and Barzilay, R · 2010
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Driving semantic parsing from the world’s response
Clarke, J., Goldwasser, D., Chang, M.-W., and Roth, D · 2010
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Learning to win by reading manuals in a monte-carlo framework
Branavan, S., Silver, D., and Barzilay, R · 2012
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Devise: a deep visual-semantic embedding model
Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., and Mikolov, T · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C · 2013
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Zero-shot learning and clustering for semantic utterance classification
Dauphin, Y. N., Tür, G., Hakkani-Tür, D., and Heck, L. P · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
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Label-embedding for image classification
Akata, Z., Perronnin, F., Harchaoui, Z., and Schmid, C · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
Lei Ba, J., Swersky, K., Fidler, S., et al · 2015
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Program synthesis using natural language
Desai, A., Gulwani, S., Hingorani, V., Jain, N., Karkare, A., Marron, M., and Roy, S · 2016
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All-in text: Learning document, label, and word representations jointly
Nam, J., Mencía, E. L., and Fürnkranz, J · 2016
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Less is more: zero-shot learning from online textual documents with noise suppression
Qiao, R., Liu, L., Shen, C., and Van Den Hengel, A · 2016
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Learning deep representations of fine-grained visual descriptions
Reed, S., Akata, Z., Lee, H., and Schiele, 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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Attributes2classname: A discriminative model for attribute-based unsupervised zero-shot learning
Demirel, B., Gokberk Cinbis, R., and Ikizler-Cinbis, N · 2017
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Image-to-markup generation with coarse-to-fine attention
Deng, Y., Kanervisto, A., Ling, J., and Rush, A. M · 2017
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Denil, M., Colmenarejo, S. G., Cabi, S., Saxton, D., and de Freitas, N · 2017
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Teaching machines to describe images with natural language feedback
Fidler, S. et al · 2017
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Colbert: Efficient and effective passage search via contextualized late interaction over bert
Khattab, O. and Zaharia, M · 2020
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Concept bottleneck models
Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., and Liang, P · 2020
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Big transfer (bit): General visual representation learning
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N · 2020
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Alice: Active learning with contrastive natural language explanations
Liang, W., Zou, J., and Yu, Z · 2020
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Named entity recognition without labelled data: A weak supervision approach
Lison, P., Barnes, J., Hubin, A., and Touileb, S · 2020
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Shaping visual representations with language for few-shot classification
Mu, J., Liang, P., and Goodman, N · 2020
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Loshchilov, I. and Hutter, F · 2017
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Snorkel: Rapid training data creation with weak supervision
Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., and Ré, C · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
Joint concept learning and semantic parsing from natural language explanations
Srivastava, S., Labutov, I., and Mitchell, T · 2017
Cited alongside, same era.
Learning with latent language
Andreas, J., Klein, D., and Levine, S · 2018
Cited alongside, same era.
Training classifiers with natural language explanations
Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., and Ré, C · 2018
Cited alongside, same era.
Grounding language for transfer in deep reinforcement learning
Narasimhan, K., Barzilay, R., and Jaakkola, T · 2018
Cited alongside, same era.
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Expbert: Representation engineering with natural language explanations
Murty, S., Koh, P. W., and Liang, P · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Weakly supervised sequence tagging from noisy rules
Safranchik, E., Luo, S., and Bach, S · 2020
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Communicating natural programs to humans and machines
Acquaviva, S., Pu, Y., Kryven, M., Wong, C., Ecanow, G. E., Nye, M., Sechopoulos, T., Tessler, M. H., and Tenenbaum, J. B · 2021
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Large-scale zero-shot image classification from rich and diverse textual descriptions
Bujwid, S. and Sullivan, J · 2021
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Coil: Revisit exact lexical match in information retrieval with contextualized inverted list
Gao, L., Dai, Z., and Callan, J · 2021
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Grounding language to entities and dynamics for generalization in reinforcement learning
Hanjie, A. W., Zhong, V. Y., and Narasimhan, K · 2021
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Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2021
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Decaf: Deep extreme classification with label features
Mittal, A., Dahiya, K., Agrawal, S., Saini, D., Agarwal, S., Kar, P., and Varma, M · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Schick, T. and Schütze, H · 2021
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Skill induction and planning with latent language
Sharma, P., Torralba, A., and Andreas, J · 2021
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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
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Leveraging language to learn program abstractions and search heuristics
Wong, C., Ellis, K. M., Tenenbaum, J., and Andreas, J · 2021
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mt5: A massively multilingual pre-trained text-to-text transformer
Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., and Raffel, C · 2021
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Power thesaurus
Power-Thesaurus · 2022
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Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Le Scao, T., Raja, A., et al · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
Zeng, A., Wong, A., Welker, S., Choromanski, K., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., Lee, J., Vanhoucke, V., et al · 2022
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