Publication
Journal papers
Journal papers
- S. Maya, K. Ueno, and T. Nishikawa. dLSTM: a new approach for anomaly detection using deep learning with delayed prediction. International Journal of Data Science and Analytics, 2019, pp:1-28.(paper)
- Y. Toyama, K. Yoshioka, K. Ban, S. Maya, A. Sai, and K. Onizuka. An 8 Bit 12.4 TOPS/W Phase-Domain MAC Circuit for Energy-Constrained Deep Learning Accelerators. IEEE Journal of Solid-State Circuits, 2019.(paper)
Conference papers (referred)
Conference papers (referred)
- S. Maya, A. Yamaguchi, K. Nishino, and K. Ueno. Lag-Aware Multivariate Time-Series Segmentation, SIAM SDM, 2020.(paper)
- A. Yamaguchi, S. Maya, K. Maruchi, K. Ueno. Learning Time-series Shapelets for Optimizing Partial AUC, SIAM SDM, 2020.(paper)
- A. Yamaguchi, S. Maya, T. Inagi, and K. Ueno. OPOSSAM: Online Prediction of Stream Data Using Self-adaptive Memory. 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. (paper)
- K. Yoshioka, Y. Toyama, K. Ban, D. Yashima, S. Maya, A. Sai, and K. Onizuka. PhaseMAC: A 14 TOPS/W 8bit GRO based Phase Domain MAC Circuit for In-Sensor-Computed Deep Learning Accelerators. 2018 IEEE Symposium on VLSI Circuits. IEEE, 2018. (paper)
- S. Maya, K. Morino, H. Murata, R. Asaoka, and K. Yamanishi. Discovery of glaucoma progressive patterns using hierarchical MDL-based clustering. In Proceeding of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. (paper) (video)
- S. Maya, K. Morino, and K. Yamanishi. Predicting glaucoma progression using multi-task learning with heterogeneous features. In Proceeding of the 2014 IEEE International Conference on Big Data (Big Data). (paper)
Workshop papers (referred)
Workshop papers (referred)
- S. Maya and K. Ueno. DADIL: Data Augmentation for Domain-Invariant Learning. Utility-Driven Mining, ACM SIGKDD workshop, 2018. (paper)
- S. Maya, K. Ueno, and T. Nishikawa. dLSTM: a new approach for anomaly detection using deep learning with delayed prediction. BigMine, ACM SIGKDD workshop, 2017. (video)
Conference papers (non-referred)
Conference papers (non-referred)
- S. Maya, A. Yamaguchi, T. Inagi, and K. Ueno. Flexible segmentation for multi-dimensional time series data (in Japanese). The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, 2019. (paper)
- S. Maya, T. Koiso, and K. Ueno. A method to identify disease-related SNP combinations using mutual information ( in Japanese). SIG-FPAI, The Japanese Society for Artificial Intelligence, 2016. (paper)