KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017

Anomaly Detection with Robust Deep Autoencoders

KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 zc8340311/RobustAutoencoder

Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains.

ANOMALY DETECTION DENOISING

Patient Subtyping via Time-Aware LSTM Networks

KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 illidanlab/T-LSTM

We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.

MULTIVARIATE TIME SERIES FORECASTING