NEC’s AI (Artificial Intelligence) Research
- 2018/02/22NEC uses AI to optimize inventory and maintenance with highly precise predictions
- 2018/02/09Sumitomo Electric and NEC Collaborate in AI- and IoT-based Mobility Business
- 2017/06/21NEC participates in INTERPOL World 2017
- 2017/04/20NEC software uses facial recognition to verify mobile service users
- 2016/09/02NEC and the University of Tokyo embark on industry-academia alliance for strengthening innovation
- 2016/09/02NEC Develops AI-based Customer Profile Estimation Technology
- 2016/07/19NEC announces new AI technology brand,"NEC the WISE"
- 2016/05/26NEC Develops Distributed Heterogeneous Mixture Learning Technology on Spark that Rapidly Discovers Patterns Hidden in Super-Large-Scale Data
- 2016/04/04NEC and Osaka University Establish Research Institute for Brain-Inspired Computing
- 201511/11 NEC strengthens AI-related business
- 2017/08/29Our paper on Customer Profile Estimation has been accepted to The IEEE International Conference on Data Mining (ICDM) as a regular paper.
Masafumi Oyamada and Shinji Nakadai, Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data, IEEE International Conference on Data Mining (ICDM) 2017
- 2017/01/12Ryohei Fujimaki (Ph. D), Research Fellow appeard at the panel discussion of “Artificial Intelligence and U.S.-Japan Alliance Engagement” symposium.
- 2016/06/24We become a sponsor of the Conference on Knowledge Discovery and Data Mining (KDD'2016).
- 2016/04/13We released the Presentation Summary and Full Transcript of Gartner Business Intelligence & Analytics Summit.
- 2016/03/30We released the Flash Report of Gartner Business Intelligence & Analytics Summit.
Further details and complete transcript of this presentation will be updated in April.
- 2016/03/03Ryohei Fujimaki (Ph. D), Research Fellow will lead a presentation entitled “Prescriptive Analysis: the Marriage of Your Business and Data Science” on March 16th from 2:00 p.m. to 2:30 p.m. (CST) at Texas C.
Event information: Gartner Business Intelligence & Analytics Summit
14 - 16 March 2016 | Grapevine, TX
Click here to view the latest information of our session.
- 2016/02/25NEC exhibited in Mobile World Congress 2016.
Click here to watch the video about the customer retention solution by our big data analysis technologies called HML (Heterogeneous Mixture Learning).
- 2017/09/15Zhao Song, Yusuke Muraoka, Ryohei Fujimaki, Lawrence Carin, Scalable Model Selection for Belief Networks, Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), 2017
- 2017/09/15Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi, Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation, Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), 2017
- 2017/08/29Masafumi Oyamada, Shinji Nakadai, Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data, IEEE International Conference on Data Mining (ICDM), 2017
- 2016/11/29Daniel Andrade, Bing Bai, Ramkumar Rajendran and Yotaro Watanabe. Analogy-based Reasoning with Memory Networks for Future Prediction. In Proceedings of the Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (CoCo) at NIPS 2016, Barcelona, Spain, 2016.
- 2016/08/24Ito and Fujimaki, Large-scale Price Optimization via Network Flow, Annual Conference on Neural Information Processing Systems (NIPS), 2016.
- 2016/05/12Masato Asahara, Ryohei Fujimaki, "Distributed Heterogeneous Mixture Learning On Spark", Spark Summit 2016.
- 2016/05/12Masato Asahara, Ryohei Fujimaki, "Big Data Heterogeneous Mixture Learning on Spark", Hadoop Summit San Jose, 2016.
- 2016/05/12Haichuan Yang, Ryohei Fujimaki, Yukitaka Kusumura, Ji Liu, "Online Feature Selection: A Limited-Memory Substitution Algorithm and its Asynchronous Parallel Variation", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
- 2015/08/10Jialei Wang, Ryohei Fujimaki, Yosuke Motohashi, “Trading Interpretability for Accuracy: Oblique Treed Sparse Additive Models”, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015