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[1] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[2] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[3] Ruifan Li,et al. Deep correspondence restricted Boltzmann machine for cross-modal retrieval , 2015, Neurocomputing.
[4] Juhan Nam,et al. Multimodal Deep Learning , 2011, ICML.
[5] Lotfi A. Zadeh,et al. A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination , 1985, AI Mag..
[6] Rajesh P. N. Rao,et al. Learning Shared Latent Structure for Image Synthesis and Robotic Imitation , 2005, NIPS.
[7] David Duvenaud,et al. Inference Suboptimality in Variational Autoencoders , 2018, ICML.
[8] Yong Deng,et al. Generalized evidence theory , 2014, Applied Intelligence.
[9] Nicolas Bousquet,et al. Diagnostics of prior-data agreement in applied Bayesian analysis , 2008 .
[10] Sidney S. Simon,et al. Merging of the Senses , 2008, Front. Neurosci..
[11] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[12] Arthur P. Dempster,et al. Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[13] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[14] Masahiro Suzuki,et al. Joint Multimodal Learning with Deep Generative Models , 2016, ICLR.
[15] H. Hotelling. Relations Between Two Sets of Variates , 1936 .
[16] Ming-Hsuan Yang,et al. Max-Margin Boltzmann Machines for Object Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Noah D. Goodman,et al. Amortized Inference in Probabilistic Reasoning , 2014, CogSci.
[18] John Shawe-Taylor,et al. Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.
[19] Jeff A. Bilmes,et al. Deep Canonical Correlation Analysis , 2013, ICML.
[20] Glenn Shafer,et al. A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.
[21] Mike Wu,et al. Multimodal Generative Models for Scalable Weakly-Supervised Learning , 2018, NeurIPS.
[22] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[23] Roger Levy,et al. A new approach to cross-modal multimedia retrieval , 2010, ACM Multimedia.
[24] Louis-Philippe Morency,et al. Multimodal Machine Learning: A Survey and Taxonomy , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Michael I. Jordan,et al. A Probabilistic Interpretation of Canonical Correlation Analysis , 2005 .
[26] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[27] Catherine K. Murphy. Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..
[28] Thierry Denoeux,et al. Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence , 2008, Artif. Intell..
[29] Chunhe Xie,et al. Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis , 2016, Sensors.
[30] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[31] Fakhri Karray,et al. Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.
[32] Pietro Perona,et al. Caltech-UCSD Birds 200 , 2010 .
[33] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[34] Nitish Srivastava,et al. Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..
[35] Kevin Murphy,et al. Generative Models of Visually Grounded Imagination , 2017, ICLR.