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Convolutional neural networks (CNNs) have shown their promising performance for natural language processing tasks, which extract n-grams as features to represent the input. However, n-gram based CNNs are inherently limited to fixed geometric structure and cannot proactively adapt to the transformations of features. In this paper, we propose two modules to provide CNNs with the flexibility for comp
Gated Probabilistic Matrix Factorization: Learning Users’ Attention from Missing Values Shohei Ohsawa, Yachiko Obara, Takayuki Osogami IBM Research – Tokyo 19–21 Nihonbashi, Hakozaki-cho, Chuo-ku, Tokyo, Japan {ohsawrks, obara, osogami}@jp.ibm.com Abstract Recommender systems rely on techniques of pre- dicting the ratings that users would give to yet un- consumed items. Probabilistic matrix factor
Deep Learning for Event-Driven Stock Prediction Xiao Ding†∗ , Yue Zhang‡ , Ting Liu† , Junwen Duan† † Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, China {xding, tliu, jwduan}@ir.hit.edu.cn ‡ Singapore University of Technology and Design yue zhang@sutd.edu.sg Abstract We propose a deep learning method for event- driven stock market prediction. First
Latent Variable Perceptron Algorithm for Structured Classification Xu Sun† Takuya Matsuzaki† Daisuke Okanohara† Jun’ichi Tsujii†‡§ † Department of Computer Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan ‡ School of Computer Science, University of Manchester, UK § National Centre for Text Mining, UK {sunxu, matuzaki, hillbig, tsujii}@is.s.u-tokyo.ac.jp Abstract We propos
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence IJCAI-09 Contents Preface / vi IJCAI-09 Conference Organization / vii IJCAI-09 Sponsorship / xvi IJCAI-09 Awards and Distinguished Papers / xvii IJCAI-09 Keynote and Invited Speakers / xix Events Colocated with IJCAI-09 / xxi IJCAI Organization / xxii AAAI Organization / xxiv Past IJCAI Conferences / xxvi ISB
International Joint Conferences on Artificial Intelligence is a non-profit corporation founded in California, in 1969 for scientific and educational purposes, including dissemination of information on Artificial Intelligence at conferences in which cutting-edge scientific results are presented and through dissemination of materials presented at these meetings in form of Proceedings, books, video r
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