most citedRLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning

23 citations · 24 across the 2 of their papers we have counts for

collaborators

3 papers

cs.LG2021★ 1 cited

Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

Tahiya Chowdhury, Murtadha Aldeer, Shantanu Laghate +1

Timeseries partitioning is an essential step in most machine-learning driven, sensor-based IoT applications. This paper introduces a sample-efficient, robust, time-series segmentat…

cs.LG2021★ 23 cited

RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning

Tong Wu, Jorge Ortiz

We introduce a new semi-supervised, time series anomaly detection algorithm that uses deep reinforcement learning (DRL) and active learning to efficiently learn and adapt to anomal…

cs.LG2019

SECRET: Semantically Enhanced Classification of Real-world Tasks

Ayten Ozge Akmandor, Jorge Ortiz, Irene Manotas +2

Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to…