12 citations · 14 across the 6 of their papers we have counts for
7 papers
TESTNAV: Pareto-Guided Search for Compositional Robustness Testing
Arooj Arif, Tobias Hartung, Elena Botoeva +1
Deep learning models remain vulnerable to real-world input perturbations, especially when multiple corruptions co-occur in the same input (e.g., brightness shifts and motion blur).…
TestifAI: Tomography-Based Testing for Deep Learning Systems
Arooj Arif, Tobias Hartung, Elena Botoeva +1
As AI systems are increasingly deployed in safety-critical application domains (e.g., autonomous driving), associated risks increase too. Deep learning models underlying modern AI…
Air Quality Downscaling with Station-Guided Pseudo-Supervision
Guorun Wang, Simone Foti, Andreas D. Demou +5
Super-resolving coarse atmospheric fields to local PM variations is uniquely challenged by a mismatch in spatial support: while pixels represent regional averages, ground-t…
DermaFlux: Synthetic Skin Lesion Generation with Rectified Flows for Enhanced Image Classification
Stathis Galanakis, Alexandros Koliousis, Stefanos Zafeiriou
Despite recent advances in deep generative modeling, skin lesion classification systems remain constrained by the limited availability of large, diverse, and well-annotated clinica…
NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
Konstantinos Barmpas, Na Lee, Dimitrios Chalatsis +7
Biosignals such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) encode physiological activity across multiple temporal and spectral scales, y…
GroupBERT: Enhanced Transformer Architecture with Efficient Grouped Structures
Ivan Chelombiev, Daniel Justus, Douglas Orr +4
Attention based language models have become a critical component in state-of-the-art natural language processing systems. However, these models have significant computational requi…
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers
Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich +3
Deep learning models are trained on servers with many GPUs, and training must scale with the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel synchr…