Fetching the paper…
Reading the bibliography…
We deal with the \textit{selective classification} problem (supervised-learning problem with a rejection option), where we want to achieve the best performance at a certain level of coverage of the data.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
Earlier work this paper cites.
Portfolio selection
Harry Markowitz · 1952
Earlier work this paper cites.
An optimum character recognition system using decision functions
Chi-Keung Chow · 1957
Earlier work this paper cites.
Universal portfolios
Thomas M. Cover · 1991
Earlier work this paper cites.
A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
Earlier work this paper cites.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
Earlier work this paper cites.
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Thomas M. Cover and Joy A. Thomas · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li jia Li, Kai Li, and Li Fei-fei · 2009
Earlier work this paper cites.
Algorithms for reinforcement learning
Csaba Szepesvári · 2009
Earlier work this paper cites.
On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
Earlier work this paper cites.
Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Cited alongside, same era.
Searching for exotic particles in high-energy physics with deep learning
Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
Cited alongside, same era.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 2017
Later among the works it cites.
Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 2017
Later among the works it cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Later among the works it cites.
Large margin deep networks for classification
Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yarin Gal and Zoubin Ghahramani · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Very deep convolutional neural network based image classification using small training sample size
Shuying Liu and Weihong Deng · 2015
Cited alongside, same era.
Uncertainty in deep learning
Yarin Gal · 2016
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2016
Cited alongside, same era.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Cited alongside, same era.
Deep learning for computational chemistry
Garrett B Goh, Nathan O Hodas, and Abhinav Vishnu · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Later among the works it cites.
Yotam Hechtlinger, Barnabás Póczos, and Larry Wasserman · 2018
Later among the works it cites.
Deep learning—a technology with the potential to transform health care
Geoffrey Hinton · 2018
Later among the works it cites.
Multimodal language analysis with recurrent multistage fusion
Paul Pu Liang, Ziyin Liu, Amir Zadeh, and Louis-Philippe Morency · 2018
Later among the works it cites.
Uncertainty in neural networks: Bayesian ensembling
Tim Pearce, Mohamed Zaki, Alexandra Brintrup, and Andy Neel · 2018
Later among the works it cites.
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
Later among the works it cites.
Dendritic cortical microcircuits approximate the backpropagation algorithm
João Sacramento, Rui Ponte Costa, Yoshua Bengio, and Walter Senn · 2018
Later among the works it cites.
Selectivenet: A deep neural network with an integrated reject option
Yonatan Geifman and Ran El-Yaniv · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Closest in time.