4 citations · 11 across the 9 of their papers we have counts for
10 papers
MergeOver: Post-Training Token Merging for Recursive Vision Transformers
Junseo Kim, Uraz Odyurt, Amirreza Yousefzadeh
Vision Transformers (ViTs) demonstrate exceptional performance in computer vision but suffer from large parameter counts and quadratic computational complexity, severely limiting t…
Implementing CPSLint: A Data Validation and Sanitisation Tool for Industrial Cyber-Physical Systems
Uraz Odyurt, Ömer Sayilir, Mariëlle Stoelinga +1
Raw datasets are often too large and unstructured to work with directly, and require a data preparation phase. The domain of industrial Cyber-Physical Systems (CPSs) is no exceptio…
TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction
Sascha Caron, Nadezhda Dobreva, Maarten Kimpel +6
High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data neces…
TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs
Arjan Blankestijn, Uraz Odyurt, Amirreza Yousefzadeh
The Transformer Machine Learning (ML) architecture has been gaining considerable momentum in recent years. In particular, computational High-Energy Physics tasks such as jet taggin…
InfoPos: A Design Support Framework for ML-Assisted Fault Detection and Identification in Industrial Cyber-Physical Systems
Uraz Odyurt, Richard Loendersloot, Tiedo Tinga
The variety of building blocks and algorithms incorporated in data-centric and ML-assisted fault detection and identification solutions is high, contributing to two challenges: sel…
TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era
Sascha Caron, Nadezhda Dobreva, Antonio Ferrer Sánchez +5
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increa…
Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering
Uraz Odyurt, Nadezhda Dobreva, Zef Wolffs +6
Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle tra…
Model Parallelism on Distributed Infrastructure: A Literature Review from Theory to LLM Case-Studies
Felix Brakel, Uraz Odyurt, Ana-Lucia Varbanescu
Neural networks have become a cornerstone of machine learning. As the trend for these to get more and more complex continues, so does the underlying hardware and software infrastru…
Towards Seamless Serverless Computing Across an Edge-Cloud Continuum
Emilian Simion, Yuandou Wang, Hsiang-ling Tai +2
Serverless computing has emerged as an attractive paradigm due to the efficiency of development and the ease of deployment without managing any underlying infrastructure. Neverthel…
Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research
Uraz Odyurt, Stephen Nicholas Swatman, Ana-Lucia Varbanescu +1
Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions,…