128 citations · 139 across the 12 of their papers we have counts for
5 papers · 1 filter
Quantile-based Maximum Likelihood Training for Outlier Detection
Masoud Taghikhah, Nishant Kumar, Siniša Šegvić +2
Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in appl…
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Alexander Timans, Nina Wiedemann, Nishant Kumar +2
Despite the strong predictive performance of deep learning models for traffic prediction, their widespread deployment in real-world intelligent transportation systems has been rest…
Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion
Nishant Kumar, Lukas Krause, Thomas Wondrak +3
Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption…
Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis
Elias Werner, Nishant Kumar, Matthias Lieber +3
Concept drift detection is crucial for many AI systems to ensure the system's reliability. These systems often have to deal with large amounts of data or react in real-time. Thus,…
Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
Nishant Kumar, Siniša Šegvić, Abouzar Eslami +1
Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to…