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We analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text.
An analysis of visual question answering algorithms
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Jia-Hong Huang, Cuong Duc Dao, Modar Alfadly, and Bernard Ghanem. 2017 · 2017
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Robin Jia and Percy Liang. 2017 · 2017
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Show, ask, attend, and answer: A strong baseline for visual question answering
Vahid Kazemi and Ali Elqursh. 2017 · 2017
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Neural semantic parsing with type constraints for semi-structured tables
Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner. 2017 · 2017
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Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, and Dario Amodei. 2017 · 2017
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Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2016 · 2016
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Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. 2016 · 2016
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Nothing else matters: model-agnostic explanations by identifying prediction invariance
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016a
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016b
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Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Damien Teney, Peter Anderson, Xiaodong He, and Anton van den Hengel. 2017 · 2017
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It was the training data pruning too!
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere. 2018 · 2018
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Fast and accurate reading comprehension by combining self-attention and convolution
Adams Wei Yu, David Dohan, Quoc Le, Thang Luong, Rui Zhao, and Kai Chen. 2018 · 2018
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