Chalapathy, Raghavendra

1. Deep Learning for Anomaly Detection: A Survey
   2019/01/10 by Raghavendra Chalapathy, Sanjay Chawla, Chalapathy, Raghavendra +1 · 2 voices · 49 citations
   Computer Science · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Network Security and Intrusion Detection #cs.LG #stat.ML
2. Anomaly Detection using One-Class Neural Networks
   2018/02/18 by Raghavendra Chalapathy, Chalapathy, Raghavendra, Aditya Krishna Menon +3 · 11 citations
   Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Network Security and Intrusion Detection #Neural and Evolutionary Computing (cs.NE)
3. Bidirectional LSTM-CRF for Clinical Concept Extraction
   2016/10/19 by Raghavendra Chalapathy, Ehsan Zare Borzeshi, Chalapathy, Raghavendra +3 · 2 citations
   Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling
4. Robust, Deep and Inductive Anomaly Detection
   2017/04/22 by Chalapathy, Raghavendra, Menon, Aditya Krishna, Chawla, Sanjay · 2 citations
   #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
5. An Investigation of Recurrent Neural Architectures for Drug Name Recognition
   2016/09/24 by Chalapathy, Raghavendra, Borzeshi, Ehsan Zare, Piccardi, Massimo · 1 citation
   #Computation and Language (cs.CL) #FOS: Computer and information sciences
6. Group Anomaly Detection using Deep Generative Models
   2018/04/13 by Raghavendra Chalapathy, Chalapathy, Raghavendra, Edward Toth +3 · 2 citations
   Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial Immune Systems Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Network Security and Intrusion Detection