3.6k citations
- The University of TokyoJP46 papers
- Japan Science and Technology AgencyJP38 papers
- RIKENJP37 papers
- National Institute of Advanced Industrial Science and TechnologyJP18 papers
- RIKEN Center for Emergent Matter ScienceJP18 papers
- Massachusetts Institute of TechnologyUS17 papers
- Tokyo Institute of TechnologyJP17 papers
- Tohoku UniversityJP16 papers
- RIKEN Advanced Science InstituteJP13 papers
- NEC (United States)US12 papers
- MIT Lincoln LaboratoryUS11 papers
- Tokyo Medical and Dental UniversityJP11 papers
23 papers · 1 filter
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Chuxu Zhang, Dongjin Song, Yuncong Chen +7
Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in mult…
A Query System for Efficiently Investigating Complex Attack Behaviors for Enterprise Security
Peng Gao, Xusheng Xiao, Zhichun Li +4
The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely at…
Extremely Dense Cores associated with Chandra Sources in Ophiuchus A: Forming Brown Dwarfs Unveiled?
Ryohei Kawabe, Chihomi Hara, Fumitaka Nakamura +15
On the basis of various data such as ALMA, JVLA, Chandra, {\it Herschel}, and {\it Spitzer}, we confirmed that two protostellar candidates in Oph-A are bona fide protostars or prot…
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients
Takuya Hiraoka, Takashi Onishi, Takahisa Imagawa +1
Hierarchical planners that produce interpretable and appropriate plans are desired, especially in its application to supporting human decision making. In the typical development of…
Solving Non-identifiable Latent Feature Models
Ryota Suzuki, Shingo Takahashi, Murtuza Petladwala +1
Latent feature models (LFM)s are widely employed for extracting latent structures of data. While offering high, parameter estimation is difficult with LFMs because of the combinati…
Attention Mechanism in Speaker Recognition: What Does It Learn in Deep Speaker Embedding?
Qiongqiong Wang, Koji Okabe, Kong Aik Lee +2
This paper presents an experimental study on deep speaker embedding with an attention mechanism that has been found to be a powerful representation learning technique in speaker re…