4 citations · 8 across the 4 of their papers we have counts for
5 papers
Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification
Ruhaan Singh, Sreelekha Guggilam
Deep learning models have proven to be effective on medical datasets for accurate diagnostic predictions from images. However, medical datasets often contain noisy, mislabeled, or…
Large Deviations for Accelerating Neural Networks Training
Sreelekha Guggilam, Varun Chandola, Abani Patra
Artificial neural networks (ANNs) require tremendous amount of data to train on. However, in classification models, most data features are often similar which can lead to increase…
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle
Sreelekha Guggilam, Varun Chandola, Abani Patra
Most current anomaly detection methods suffer from the curse of dimensionality when dealing with high-dimensional data. We propose an anomaly detection algorithm that can scale to…
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)
Sreelekha Guggilam, Syed M. A. Zaidi, Varun Chandola +1
Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with…
Bayesian Anomaly Detection Using Extreme Value Theory
Sreelekha Guggilam, S. M. Arshad Zaidi, Varun Chandola +1
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is…