5 citations · 7 across the 4 of their papers we have counts for
5 papers
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
Interpretable Tensor Fusion
Saurabh Varshneya, Antoine Ledent, Philipp Liznerski +4
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse type…
Reimagining Anomalies: What If Anomalies Were Normal?
Philipp Liznerski, Saurabh Varshneya, Ece Calikus +5
Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is pr…
Learning Interpretable Concept Groups in CNNs
Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen +4
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept…
Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks
Andreas Kölsch, Ashutosh Mishra, Saurabh Varshneya +2
This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotation…