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The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different modalities.
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The somatic genomic landscape of glioblastoma
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Sinkhorn distances: Lightspeed computation of optimal transport
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The cancer genome atlas pan-cancer analysis project
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Quantifying unique information
Nils Bertschinger, Johannes Rauh, Eckehard Olbrich, Jürgen Jost, and Nihat Ay · 2014
Detecting statistical interactions from neural network weights
Michael Tsang, Dehua Cheng, and Yan Liu · 2018
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Multimodal generative models for scalable weakly-supervised learning
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Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph
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Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, and Soujanya Poria · 2019
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Ur-funny: A multimodal language dataset for understanding humor
Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, and Mohammed Ehsan Hoque · 2019
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Improving the short-term prediction of suicidal behavior
Catherine R Glenn and Matthew K Nock · 2014
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Quantifying synergistic mutual information
Virgil Griffith and Christof Koch · 2014
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Very deep convolutional networks for large-scale image recognition, 2014
Karen Simonyan and Andrew Zisserman · 2014
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Vqa: Visual question answering
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Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas
Cancer Genome Atlas Research Network · 2015
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The cancer genome atlas (tcga): an immeasurable source of knowledge
Katarzyna Tomczak, Patrycja Czerwińska, and Maciej Wiznerowicz · 2015
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Deep multimodal multilinear fusion with high-order polynomial pooling
Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, and Qibin Zhao · 2019
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Learning representations from imperfect time series data via tensor rank regularization
Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2019
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Roberta: A robustly optimized bert pretraining approach, 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Feature interaction interpretability: A case for explaining ad-recommendation systems via neural interaction detection
Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, and Yan Liu · 2019
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Idtxl: The information dynamics toolkit xl: a python package for the efficient analysis of multivariate information dynamics in networks
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CDC WONDER: Underlying cause of death, 1999–2019
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Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
Richard J Chen, Ming Y Lu, Jingwen Wang, Drew FK Williamson, Scott J Rodig, Neal I Lindeman, and Faisal Mahmood · 2020
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An information-theoretic quantification of discrimination with exempt features
Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, and Pulkit Grover · 2020
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Does my multimodal model learn cross-modal interactions? it’s harder to tell than you might think!
Jack Hessel and Lillian Lee · 2020
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Multiplicative interactions and where to find them
Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, and Razvan Pascanu · 2020
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Multimodal sensor fusion with differentiable filters
Michelle A Lee, Brent Yi, Roberto Martín-Martín, Silvio Savarese, and Jeannette Bohg · 2020
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What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Multimodal transformer for multimodal machine translation
Shaowei Yao and Xiaojun Wan · 2020
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Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
Richard J Chen, Ming Y Lu, Wei-Hung Weng, Tiffany Y Chen, Drew FK Williamson, Trevor Manz, Maha Shady, and Faisal Mahmood · 2021
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Jan Ittner, Lukasz Bolikowski, Konstantin Hemker, and Ricardo Kennedy · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
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Redundant information neural estimation
Michael Kleinman, Alessandro Achille, Stefano Soatto, and Jonathan C Kao · 2021
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Semi-supervised aggregation of dependent weak supervision sources with performance guarantees
Alessio Mazzetto, Dylan Sam, Andrew Park, Eli Upfal, and Stephen Bach · 2021
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Estimating the unique information of continuous variables
Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, and Elad Schneidman · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2021
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M2lens: Visualizing and explaining multimodal models for sentiment analysis
Xingbo Wang, Jianben He, Zhihua Jin, Muqiao Yang, Yong Wang, and Huamin Qu · 2021
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Patricia Wollstadt, Sebastian Schmitt, and Michael Wibral · 2021
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Tutorial on amortized optimization for learning to optimize over continuous domains
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MOSEK Optimizer API for Python 10.0.34 , 2022
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Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Richard J Chen, Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Jana Lipkova, Zahra Noor, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, et al · 2022
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Cooperative learning for multiview analysis
Daisy Yi Ding, Shuangning Li, Balasubramanian Narasimhan, and Robert Tibshirani · 2022
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Gacs-korner common information variational autoencoder
Michael Kleinman, Alessandro Achille, Stefano Soatto, and Jonathan Kao · 2022
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Artificial intelligence for multimodal data integration in oncology
Jana Lipkova, Richard J Chen, Bowen Chen, Ming Y Lu, Matteo Barbieri, Daniel Shao, Anurag J Vaidya, Chengkuan Chen, Luoting Zhuang, Drew FK Williamson, et al · 2022
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Dime: Fine-grained interpretations of multimodal models via disentangled local explanations
Yiwei Lyu, Paul Pu Liang, Zihao Deng, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2022
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Alexandra M Proca, Fernando E Rosas, Andrea I Luppi, Daniel Bor, Matthew Crosby, and Pedro AM Mediano · 2022
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Multimodal learning with transformers: A survey
Peng Xu, Xiatian Zhu, and David A Clifton · 2022
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Demystifying local and global fairness trade-offs in federated learning using information theory
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