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Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset.
The use of multiple measurements in taxonomic problems
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Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal · 1997
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Gradient-based learning applied to document recognition
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Object Recognition with Gradient-Based Learning , pages 319–345
Yann LeCun, Patrick Haffner, Léon Bottou, and Yoshua Bengio · 1999
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The TREC-8 question answering track report
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2001
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Pruning training sets for learning of object categories
Anelia Angelova, Yaser Abu-Mostafam, and Pietro Perona · 2005
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Core vector machines: Fast SVM training on very large data sets
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung · 2005
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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A study of the robustness of knn classifiers trained using soft labels
Neamat El Gayar, Friedhelm Schwenker, and Günther Palm · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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A taxonomy and experimental study on prototype generation for nearest neighbor classification
Isaac Triguero, Joaquín Derrac, Salvador Garcia, and Francisco Herrera · 2011
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Prototype selection for nearest neighbor classification: Taxonomy and empirical study
Salvador Garcia, Joaquin Derrac, Jose Cano, and Francisco Herrera · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts · 2013
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End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
Xuezhe Ma and Eduard Hovy · 2016
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Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Active learning for convolutional neural networks: A core-set approach
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Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Sequence-level knowledge distillation
Yoon Kim and Alexander M Rush · 2016
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Ozan Sener and Silvio Savarese · 2017
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SeqGAN: sequence generative adversarial nets with policy gradient
L Yu, W Zhang, J Wang, and Y Yu · 2017
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
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BERT: Pre-training of deep bidirectional Transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
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