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With the wide development of black-box machine learning algorithms, particularly deep neural network (DNN), the practical demand for the reliability assessment is rapidly rising.
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
Ahmed Taha, Yi-Ting Chen, Xitong Yang, Teruhisa Misu, and Larry Davis. 2019 · 1901
Earlier work this paper cites.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
A Practical Bayesian Framework for Backpropagation Networks
David J. C. MacKay. 1992 · 1992
Earlier work this paper cites.
Geoffrey E. Hinton and Drew van Camp. 1993 · 1993
Earlier work this paper cites.
Ensemble Learning for Multi-Layer Networks. In Advances in Neural Information Processing Systems (NIPS)
David Barber and Christopher M Bishopt. 1997 · 1997
Earlier work this paper cites.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2003
Earlier work this paper cites.
Are Loss Functions All the Same?
Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, and Alessandro Verri. 2004 · 2004
Earlier work this paper cites.
Pattern recognition and machine learning. In Springer , Vol. 4. 738
CM Christopher M CM Bishop. 2006 · 2006
Earlier work this paper cites.
Aleatory or epistemic? Does it matter?
Armen Der Kiureghian and Ove Ditlevsen. 2009 · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 248–255
Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Equations of states in singular statistical estimation
Sumio Watanabe. 2010 · 2009
Earlier work this paper cites.
Large scale image annotation: Learning to rank with joint word-image embeddings
Jason Weston, Samy Bengio, and Nicolas Usunier. 2010 · 2010
Earlier work this paper cites.
Yoshua Bengio. 2012 · 2012
Earlier work this paper cites.
Representation Learning: A Review and New Perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
Earlier work this paper cites.
DeViSE: A Deep Visual-Semantic Embedding Model
Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc’Aurelio Ranzato, and Tomas Mikolov. 2013 · 2013
Earlier work this paper cites.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2013
Earlier work this paper cites.
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Diederik P. Kingma and Max Welling. 2014 · 2014
Cited alongside, same era.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. 1–13
Ryan Kiros, Ruslan Salakhutdinov, and Richard S. Zemel. 2014 · 2014
Cited alongside, same era.
Tsung Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014 · 2014
Cited alongside, same era.
Alex Kendall, Vijay Badrinarayanan, Roberto Cipolla, Vijay Badrinarayanan, and Roberto Cipolla. 2017 · 2017
Later among the works it cites.
Yuncheng Li, Yale Song, and Jiebo Luo. 2017 · 2017
Later among the works it cites.
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017 · 2017
Later among the works it cites.
Li Zhang, Tao Xiang, and Shaogang Gong. 2017 · 2017
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
M. H. Peter Young, Alice Lai, and J. Hockenmaier. 2014 · 2014
Cited alongside, same era.
Yaroslav Ganin and Victor Lempitsky. 2015 · 2015
Cited alongside, same era.
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Cited alongside, same era.
Andrej Karpathy and Li Fei-Fei. 2015 · 2015
Cited alongside, same era.
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii. 2015 · 2015
Cited alongside, same era.
Karen Simonyan and Andrew Zisserman. 2015 · 2015
Cited alongside, same era.
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Dual-Path Convolutional Image-Text Embedding with Instance Loss
Zhedong Zheng, Liang Zheng, Michael Garrett, Yi Yang, and Yi-Dong Shen. 2017 · 2017
Later among the works it cites.
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, and Dmitry Vetrov. 2018 · 2018
Later among the works it cites.
Fartash Faghri, David J Fleet, Jamie Ryan Kiros, and Sanja Fidler. 2018 · 2018
Later among the works it cites.
Jiuxiang Gu, Jianfei Cai, Shafiq Joty, Li Niu, and Gang Wang. 2018 · 2018
Later among the works it cites.
Anomaly Machine Component Detection by Deep Generative Model with Unregularized Score. In International Joint Conference on Neural Networks (IJCNN)
Takashi Matsubara, Ryosuke Tachibana, and Kuniaki Uehara. 2018 · 2018
Later among the works it cites.
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Later among the works it cites.
Understanding Measures of Uncertainty for Adversarial Example Detection
Lewis Smith and Yarin Gal. 2018 · 2018
Later among the works it cites.
RICAP : Random Image Cropping and Patching Data Augmentation for Deep CNNs. In Asian Conference on Machine Learning (ACML)
Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara. 2018 · 2018
Later among the works it cites.
Semih Yagcioglu, Aykut Erdem, Erkut Erdem, and Nazli Ikizler-Cinbis. 2018 · 2018
Later among the works it cites.
A Closer Look at Few-shot Classification. In International Conference on Learning Representations (ICLR)
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang. 2019 · 2019
Closest in time.
Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf. 2019 · 2019
Closest in time.
Quantifying Uncertainties in Natural Language Processing Tasks
Yijun Xiao and William Yang Wang. 2019 · 2019
Closest in time.