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Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning.

, , , , , and . CoRR, (2018)

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Image-to-Markup Generation with Coarse-to-Fine Attention., , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 980-989. PMLR, (2017)Visual Attention Model for Cross-Sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning., , , , , and . FLAIRS Conference, page 98-103. AAAI Press, (2019)Diversifying Restricted Boltzmann Machine for Document Modeling., , and . KDD, page 1315-1324. ACM, (2015)OpenNMT: Open-Source Toolkit for Neural Machine Translation., , , , and . ACL (System Demonstrations), page 67-72. Association for Computational Linguistics, (2017)Dropout with Expectation-linear Regularization., , , , , and . CoRR, (2016)Neural Machine Translation with Recurrent Attention Modeling., , , , and . EACL (2), page 383-387. Association for Computational Linguistics, (2017)Neural Linguistic Steganography., , and . EMNLP/IJCNLP (1), page 1210-1215. Association for Computational Linguistics, (2019)OpenNMT: Open-source Toolkit for Neural Machine Translation., , , , , and . CoRR, (2017)Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning., , , , , and . CoRR, (2018)What You Get Is What You See: A Visual Markup Decompiler., , and . CoRR, (2016)