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Ensembles of models often yield improvements in system performance.
Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Dataset Shift in Machine Learning
Joaquin Quiñonero-Candela · 2009
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Recurrent Neural Network Based Language Model
Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur · 2010
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
Max Welling and Yee Whye Teh · 2011
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Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, and Brian Kingsbury · 2012
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Machine Learning
Kevin P. Murphy · 2012
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Deep speech: Scaling up end-to-end speech recognition, 2014
Awni Y. Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, and Andrew Y. Ng · 2014
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Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T. Weirauch, and Brendan J. Frey · 2015
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Fast R-CNN
Ross Girshick · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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George Papamakarios · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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LSUN: construction of a large-scale image dataset using deep learning with humans in the loop, 2015
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
Cited alongside, same era.
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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Learning to Generate Long-term Future via Hierarchical Prediction
Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, and Honglak Lee · 2017
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Multi-task ensembles with teacher-student training
J. H. M. Wong and M. J. F. Gales · 2017
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Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, et al · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
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Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, and Dan Mané · 2016
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Uncertainty in Deep Learning
Yarin Gal · 2016
Cited alongside, same era.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Cited alongside, same era.
Tiny ImageNet
Stanford CS231N · 2017
Cited alongside, same era.
Andrey Malinin and Mark Gales · 2018
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Understanding Measures of Uncertainty for Adversarial Example Detection
L. Smith and Y. Gal · 2018
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Sequence teacher-student training of acoustic models for automatic free speaking language assessment
Y. Wang, J. H. M. Wong, M. J. F. Gales, K. M. Knill, and A. Ragni · 2018
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Efficient evaluation-time uncertainty estimation by improved distillation
Erik Englesson and Hossein Azizpour · 2019
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Reducing overconfident errors outside the known distribution, 2019
Zhizhong Li and Derek Hoiem · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, and Andrew Gordon Wilson · 2019
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Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment
Andrey Malinin · 2019
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Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness
Andrey Malinin and Mark Gales · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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