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In multimodal sentiment analysis (MSA), the performance of a model highly depends on the quality of synthesized embeddings.
Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter. 2019 · 1906
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Gaussian mixture model based mutual information estimation between frequency bands in speech
Mattias Nilsson, Harald Gustaftson, Søren Vang Andersen, and W Bastiaan Kleijn. 2002 · 2002
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The IM algorithm: A variational approach to information maximization
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Earlier work this paper cites.
On entropy approximation for gaussian mixture random vectors
Marco F Huber, Tim Bailey, Hugh Durrant-Whyte, and Uwe D Hanebeck. 2008 · 2008
Earlier work this paper cites.
Speaker identification on the scotus corpus
Jiahong Yuan and Mark Liberman. 2008 · 2008
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Sentiment analysis of blogs by combining lexical knowledge with text classification
Prem Melville, Wojciech Gryc, and Richard D Lawrence. 2009 · 2009
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen. 2010 · 2010
Earlier work this paper cites.
Textural feature selection by joint mutual information based on gaussian mixture model for multispectral image classification
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Earlier work this paper cites.
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Louis-Philippe Morency, Rada Mihalcea, and Payal Doshi. 2011 · 2011
Earlier work this paper cites.
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Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. 2011 · 2011
Earlier work this paper cites.
The emotions: A philosophical introduction
Julien Deonna and Fabrice Teroni. 2012 · 2012
Earlier work this paper cites.
Youtube movie reviews: Sentiment analysis in an audio-visual context
Martin Wöllmer, Felix Weninger, Tobias Knaup, Björn Schuller, Congkai Sun, Kenji Sagae, and Louis-Philippe Morency. 2013 · 2013
Earlier work this paper cites.
Covarep—a collaborative voice analysis repository for speech technologies
Gilles Degottex, John Kane, Thomas Drugman, Tuomo Raitio, and Stefan Scherer. 2014 · 2014
Earlier work this paper cites.
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Naftali Tishby and Noga Zaslavsky. 2015 · 2015
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016 · 2016
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Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages
Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis-Philippe Morency. 2016 · 2016
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Relja Arandjelovic and Andrew Zisserman. 2017 · 2017
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Multimodal sentiment analysis with word-level fusion and reinforcement learning
Minghai Chen, Sen Wang, Paul Pu Liang, Tadas Baltrušaitis, Amir Zadeh, and Louis-Philippe Morency. 2017 · 2017
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Select-additive learning: Improving generalization in multimodal sentiment analysis
Haohan Wang, Aaksha Meghawat, Louis-Philippe Morency, and Eric P Xing. 2017 · 2017
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Learning representations for neural network-based classification using the information bottleneck principle
Rana Ali Amjad and Bernhard C Geiger. 2019 · 2019
Later among the works it cites.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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DialogueGCN: A graph convolutional neural network for emotion recognition in conversation
Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, and Alexander Gelbukh. 2019 · 2019
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Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, and Barnabás Póczos. 2019 · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker. 2019 · 2019
Later among the works it cites.
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Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2017 · 2017
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Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018 · 2018
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CASCADE: Contextual sarcasm detection in online discussion forums
Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, and Rada Mihalcea. 2018 · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018 · 2018
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Efficient low-rank multimodal fusion with modality-specific factors
Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, AmirAli Bagher Zadeh, and Louis-Philippe Morency. 2018 · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Deep graph infomax
Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2018 · 2018
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Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. 2020 · 2020
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MISA: Modality-invariant and-specific representations for multimodal sentiment analysis
Devamanyu Hazarika, Roger Zimmermann, and Soujanya Poria. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, and Rada Mihalcea. 2020 · 2020
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Integrating multimodal information in large pretrained transformers
Wasifur Rahman, Md Kamrul Hasan, Sangwu Lee, AmirAli Bagher Zadeh, Chengfeng Mao, Louis-Philippe Morency, and Ehsan Hoque. 2020 · 2020
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Learning relationships between text, audio, and video via deep canonical correlation for multimodal language analysis
Zhongkai Sun, Prathusha Sarma, William Sethares, and Yingyu Liang. 2020 · 2020
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Multimodal routing: Improving local and global interpretability of multimodal language analysis
Yao-Hung Hubert Tsai, Martin Ma, Muqiao Yang, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020 · 2020
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Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu. 2021 · 2021
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