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Model distillation aims to distill the knowledge of a complex model into a simpler one.
The perceptron, a perceiving and recognizing automaton Project Para
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Teachability in computational learning
Ayumi Shinohara and Satoru Miyano · 1991
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A database for handwritten text recognition research
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Fast exact multiplication by the hessian
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Sally A Goldman and Michael J Kearns · 1995
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Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
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Support vector machines
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf · 1998
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The mnist database of handwritten digits
Yann LeCun · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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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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Dataset issues in object recognition
Jean Ponce, Tamara L Berg, Mark Everingham, David A Forsyth, Martial Hebert, Svetlana Lazebnik, Marcin Marszalek, Cordelia Schmid, Bryan C Russell, Antonio Torralba, et al · 2006
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Frustratingly easy domain adaptation
Hal Daume III · 2007
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Smaller coresets for k-median and k-means clustering
Sariel Har-Peled and Akash Kushal · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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A review of instance selection methods
J Arturo Olvera-López, J Ariel Carrasco-Ochoa, J Francisco Martínez-Trinidad, and Josef Kittler · 2010
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Object detectors emerge in deep scene cnns
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2015
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Machine teaching: An inverse problem to machine learning and an approach toward optimal education
Xiaojin Zhu · 2015
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Generic methods for optimization-based modeling
Justin Domke · 2012
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cuda-convnet: High-performance c++/cuda implementation of convolutional neural networks
Alex Krizhevsky · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Data poisoning attacks on factorization-based collaborative filtering
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik · 2016
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Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
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Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Data-free knowledge distillation for deep neural networks
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 2017
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Few-shot adversarial domain adaptation
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto · 2017
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Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Data distillation: Towards omni-supervised learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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