196 citations · 202 across the 6 of their papers we have counts for
5 papers · 1 filter
Real-time Drift Detection on Time-series Data
Nandini Ramanan, Rasool Tahmasbi, Marjorie Sayer +3
Practical machine learning applications involving time series data, such as firewall log analysis to proactively detect anomalous behavior, are concerned with real time analysis of…
Time Series Anomaly Detection with label-free Model Selection
Deokwoo Jung, Nandini Ramanan, Mehrnaz Amjadi +3
Anomaly detection for time-series data becomes an essential task for many data-driven applications fueled with an abundance of data and out-of-the-box machine-learning algorithms.…
Boosted Embeddings for Time Series Forecasting
Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi +7
Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting mo…
Enabling Incremental Training with Forward Pass for Edge Devices
Dana AbdulQader, Shoba Krishnan, Claudionor N. Coelho
Deep Neural Networks (DNNs) are commonly deployed on end devices that exist in constantly changing environments. In order for the system to maintain it's accuracy, it is critical t…
Enabling Binary Neural Network Training on the Edge
Erwei Wang, James J. Davis, Daniele Moro +6
The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Bin…