Zobrazeno 1 - 10
of 51 446
pro vyhledávání: '"A Hayat"'
The joint use of event-based vision and Spiking Neural Networks (SNNs) is expected to have a large impact in robotics in the near future, in tasks such as, visual odometry and obstacle avoidance. While researchers have used real-world event datasets
Externí odkaz:
http://arxiv.org/abs/2412.09209
In this paper, we investigate the phase transitions and critical behavior of a nonlinear magnetically charged rotating AdS black hole, with a particular emphasis on the influence of a quintessence field. Our comprehensive thermodynamic analysis explo
Externí odkaz:
http://arxiv.org/abs/2410.13308
Diffusion models excel at generative modeling (e.g., text-to-image) but sampling requires multiple denoising network passes, limiting practicality. Efforts such as progressive distillation or consistency distillation have shown promise by reducing th
Externí odkaz:
http://arxiv.org/abs/2410.11971
Autor:
Hayat, Seemab, Azam, Naveed Ahmed
Reconstruction of evolutionary relationships between species is an important topic in the field of computational biology. Pairwise compatibility graphs (PCGs) are used to model such relationships. A graph is a PCG if its edges can be represented by t
Externí odkaz:
http://arxiv.org/abs/2410.10525
Despite their spectacular progress, language models still struggle on complex reasoning tasks, such as advanced mathematics. We consider a long-standing open problem in mathematics: discovering a Lyapunov function that ensures the global stability of
Externí odkaz:
http://arxiv.org/abs/2410.08304
Autor:
Zhou, Fang, Huang, Yaning, Liang, Dong, Li, Dai, Zhang, Zhongke, Wang, Kai, Xin, Xiao, Aboelela, Abdallah, Jiang, Zheliang, Wang, Yang, Song, Jeff, Zhang, Wei, Liang, Chen, Li, Huayu, Sun, ChongLin, Yang, Hang, Qu, Lei, Shu, Zhan, Yuan, Mindi, Maccherani, Emanuele, Hayat, Taha, Guo, John, Puvvada, Varna, Pashkevich, Uladzimir
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and strict service-level agreements (SLAs). Previous research efforts have
Externí odkaz:
http://arxiv.org/abs/2410.06497
Autor:
Abdul, Wadood M, Pimentel, Marco AF, Salman, Muhammad Umar, Raha, Tathagata, Christophe, Clément, Kanithi, Praveen K, Hayat, Nasir, Rajan, Ronnie, Khan, Shadab
This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP) task of extracting structured information from clinical narratives t
Externí odkaz:
http://arxiv.org/abs/2410.05046
Autor:
Kanithi, Praveen K, Christophe, Clément, Pimentel, Marco AF, Raha, Tathagata, Saadi, Nada, Javed, Hamza, Maslenkova, Svetlana, Hayat, Nasir, Rajan, Ronnie, Khan, Shadab
The rapid development of Large Language Models (LLMs) for healthcare applications has spurred calls for holistic evaluation beyond frequently-cited benchmarks like USMLE, to better reflect real-world performance. While real-world assessments are valu
Externí odkaz:
http://arxiv.org/abs/2409.07314
Autor:
Cai, Zhixi, Dhall, Abhinav, Ghosh, Shreya, Hayat, Munawar, Kollias, Dimitrios, Stefanov, Kalin, Tariq, Usman
The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M data
Externí odkaz:
http://arxiv.org/abs/2409.06991
Autor:
Borse, Shubhankar, Kadambi, Shreya, Pandey, Nilesh Prasad, Bhardwaj, Kartikeya, Ganapathy, Viswanath, Priyadarshi, Sweta, Garrepalli, Risheek, Esteves, Rafael, Hayat, Munawar, Porikli, Fatih
While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated images, as the model tends to copy data from the observed training samples
Externí odkaz:
http://arxiv.org/abs/2406.08798