Zobrazeno 1 - 10
of 82 970
pro vyhledávání: '"A. A. Ammar"'
Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and includes bi-level optimization methods on models and syntheses, such as FRe
Externí odkaz:
http://arxiv.org/abs/2411.19946
Autor:
Fayad, Ammar
Gravitational waves (GW), predicted by Einstein's General Theory of Relativity, provide a powerful probe of astrophysical phenomena and fundamental physics. In this work, we propose an unsupervised anomaly detection method using variational autoencod
Externí odkaz:
http://arxiv.org/abs/2411.19450
We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on \textit{single-head} transformers with only a \textit{single} self-attention layer: (i) is un
Externí odkaz:
http://arxiv.org/abs/2411.16525
Chronic kidney disease (CKD) is a significant public health challenge, often progressing to end-stage renal disease (ESRD) if not detected and managed early. Early intervention, warranted by silent disease progression, can significantly reduce associ
Externí odkaz:
http://arxiv.org/abs/2411.10754
Internal crack detection has been a subject of focus in structural health monitoring. By focusing on crack detection in structural datasets, it is demonstrated that deep learning (DL) methods can effectively analyze seismic wave fields interacting wi
Externí odkaz:
http://arxiv.org/abs/2411.10389
We present Y-MAP-Net, a Y-shaped neural network architecture designed for real-time multi-task learning on RGB images. Y-MAP-Net, simultaneously predicts depth, surface normals, human pose, semantic segmentation and generates multi-label captions, al
Externí odkaz:
http://arxiv.org/abs/2411.10334
Micro Crack detection using deep neural networks (DNNs) through an automated pipeline using wave fields interacting with the damaged areas is highly sought after. These high-dimensional spatio-temporal crack data are limited, and these datasets have
Externí odkaz:
http://arxiv.org/abs/2411.10015
Autor:
Jahin, Ammar, Lin, Shi-Zeng
We study the effect of the electron wavefunction on Kohn-Luttinger superconductivity. The role of the wavefunction is encoded in a complex form factor describing the topology and geometry of the bands. We show that the electron wavefunction significa
Externí odkaz:
http://arxiv.org/abs/2411.09664
Autor:
Zhao, Liang, Geng, Shenglin, Tang, Xiongyan, Hawbani, Ammar, Sun, Yunhe, Xu, Lexi, Tarchi, Daniele
Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data colle
Externí odkaz:
http://arxiv.org/abs/2411.07752
Autor:
Liu, Puze, Günster, Jonas, Funk, Niklas, Gröger, Simon, Chen, Dong, Bou-Ammar, Haitham, Jankowski, Julius, Marić, Ante, Calinon, Sylvain, Orsula, Andrej, Olivares-Mendez, Miguel, Zhou, Hongyi, Lioutikov, Rudolf, Neumann, Gerhard, Zhalehmehrabi, Amarildo Likmeta Amirhossein, Bonenfant, Thomas, Restelli, Marcello, Tateo, Davide, Liu, Ziyuan, Peters, Jan
Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robo
Externí odkaz:
http://arxiv.org/abs/2411.05718