Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Vandierendonck, Hans"'
Model Inversion (MI) is a type of privacy violation that focuses on reconstructing private training data through abusive exploitation of machine learning models. To defend against MI attacks, state-of-the-art (SOTA) MI defense methods rely on regular
Externí odkaz:
http://arxiv.org/abs/2409.01062
Autor:
Arif, Kazi Hasan Ibn, Yoon, JinYi, Nikolopoulos, Dimitrios S., Vandierendonck, Hans, John, Deepu, Ji, Bo
High-resolution Vision-Language Models (VLMs) have been widely used in multimodal tasks to enhance accuracy by preserving detailed image information. However, these models often generate excessive visual tokens due to encoding multiple partitions of
Externí odkaz:
http://arxiv.org/abs/2408.10945
Autor:
Esfahani, Mohsen Koohi, D'Antonio, Marco, Tauhidi, Syed Ibtisam, Mai, Thai Son, Vandierendonck, Hans
Comprehensive evaluation is one of the basis of experimental science. In High-Performance Graph Processing, a thorough evaluation of contributions becomes more achievable by supporting common input formats over different frameworks. However, each fra
Externí odkaz:
http://arxiv.org/abs/2404.19735
Autor:
Esfahani, Mohsen Koohi, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna, Sebastiano
Progress in High-Performance Computing in general, and High-Performance Graph Processing in particular, is highly dependent on the availability of publicly-accessible, relevant, and realistic data sets. To ensure continuation of this progress, we (i)
Externí odkaz:
http://arxiv.org/abs/2308.16744
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is
Externí odkaz:
http://arxiv.org/abs/2210.16083
Autor:
Lee, JunKyu, Mukhanov, Lev, Molahosseini, Amir Sabbagh, Minhas, Umar, Hua, Yang, del Rincon, Jesus Martinez, Dichev, Kiril, Hong, Cheol-Ho, Vandierendonck, Hans
Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep neural networks demand substantial compute resources during training and
Externí odkaz:
http://arxiv.org/abs/2112.15131
Autor:
Minhas, Umar Ibrahim, Mukhanov, Lev, Karakonstantis, Georgios, Vandierendonck, Hans, Woods, Roger
Machine vision tasks present challenges for resource constrained edge devices, particularly as they execute multiple tasks with variable workloads. A robust approach that can dynamically adapt in runtime while maintaining the maximum quality of servi
Externí odkaz:
http://arxiv.org/abs/2108.12914
Real-time video analytics on the edge is challenging as the computationally constrained resources typically cannot analyse video streams at full fidelity and frame rate, which results in loss of accuracy. This paper proposes a Transprecise Object Det
Externí odkaz:
http://arxiv.org/abs/2105.08668
Knowledge acquisition is the essential first step of any Knowledge Graph (KG) application. This knowledge can be extracted from a given corpus (KG generation process) or specified from an existing KG (KG specification process). Focusing on domain spe
Externí odkaz:
http://arxiv.org/abs/2012.10271
Autor:
Mukhanov, Lev, Tovletoglou, Konstantinos, Vandierendonck, Hans, Nikolopoulos, Dimitrios S., Karakonstantis, Georgios
Publikováno v:
In Proceedings of the IEEE International Symposium on Workload Characterization (IISWC), Orlando, Florida, USA, 2019
The aggressive scaling of technology may have helped to meet the growing demand for higher memory capacity and density, but has also made DRAM cells more prone to errors. Such a reality triggered a lot of interest in modeling DRAM behavior for either
Externí odkaz:
http://arxiv.org/abs/2003.12448