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Multi-view classification (MVC) generally focuses on improving classification accuracy by using information from different views, typically integrating them into a unified comprehensive representation for downstream tasks.
Upper and lower probabilities induced by a multivalued mapping
AP Dempster · 1967
Earlier work this paper cites.
A generalization of bayesian inference
Arthur P Dempster · 1968
Earlier work this paper cites.
A mathematical theory of evidence , volume 42
Glenn Shafer · 1976
Earlier work this paper cites.
Transforming neural-net output levels to probability distributions
John S Denker and Yann LeCun · 1991
Earlier work this paper cites.
Relations between two sets of variates
Harold Hotelling · 1992
Earlier work this paper cites.
A simple generalisation of the area under the roc curve for multiple class classification problems
David J Hand and Robert J Till · 2001
Earlier work this paper cites.
Combination of evidence in Dempster-Shafer theory , volume 4015
Kari Sentz, Scott Ferson, et al · 2002
Earlier work this paper cites.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
Earlier work this paper cites.
A bayesian hierarchical model for learning natural scene categories
Li Fei-Fei and Pietro Perona · 2005
Earlier work this paper cites.
A kernel method for canonical correlation analysis
Shotaro Akaho · 2006
Earlier work this paper cites.
Pattern recognition and machine learning
Christopher M Bishop · 2006
Earlier work this paper cites.
Variational bayesian approach to canonical correlation analysis
Chong Wang · 2007
Earlier work this paper cites.
Bayesian theory , volume 405
José M Bernardo and Adrian FM Smith · 2009
Earlier work this paper cites.
Multimodal techniques for diagnosis and prognosis of alzheimer’s disease
Richard J Perrin, Anne M Fagan, and David M Holtzman · 2009
Earlier work this paper cites.
Introduction to the dirichlet distribution and related processes
Bela A Frigyik, Amol Kapila, and Maya R Gupta · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Hmdb: a large video database for human motion recognition
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Cited alongside, same era.
Interpretation and fusion of hyper opinions in subjective logic
Audun Jøsang and Robin Hankin · 2012
Cited alongside, same era.
Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
Cited alongside, same era.
Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
Cited alongside, same era.
Deep canonical correlation analysis
Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
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.
Uncertainty-aware attention for reliable interpretation and prediction
Jay Heo, Hae Beom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, and Sung Ju Hwang · 2018
Later among the works it cites.
Subjective Logic: A formalism for reasoning under uncertainty
Audun Jøsang · 2018
Later among the works it cites.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
Later among the works it cites.
Efficient large-scale multi-modal classification
Douwe Kiela, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2018
Later among the works it cites.
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Bayesian convolutional neural networks with bernoulli approximate variational inference
Yarin Gal and Zoubin Ghahramani · 2015
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Recipe recognition with large multimodal food dataset
Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, and Frederic Precioso · 2015
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2018
Later among the works it cites.
Multimodal neuromarkers in schizophrenia via cognition-guided mri fusion
Jing Sui, Shile Qi, Theo GM van Erp, Juan Bustillo, Rongtao Jiang, Dongdong Lin, Jessica A Turner, Eswar Damaraju, Andrew R Mayer, Yue Cui, et al · 2018
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Learning representations by maximizing mutual information across views
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Supervised multimodal bitransformers for classifying images and text
Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, and Davide Testuggine · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Cpm-nets: Cross partial multi-view networks
Changqing Zhang, Zongbo Han, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu, et al · 2019
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A simple framework for contrastive learning of visual representations
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Contrastive multi-view representation learning on graphs
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Confidence-aware learning for deep neural networks
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Uncertainty estimation using a single deep deterministic neural network
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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What makes training multi-modal classification networks hard?
Weiyao Wang, Du Tran, and Matt Feiszli · 2020
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