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pro vyhledávání: '"Rizvi, Syed A"'
Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large
Externí odkaz:
http://arxiv.org/abs/2410.19444
Autor:
Alamu, Opeyemi Sheu, Choque, Bismar Jorge Gutierrez, Rizvi, Syed Wajeeh Abbs, Hammed, Samah Badr, Medani, Isameldin Elamin, Siam, Md Kamrul, Tahir, Waqar Ahmad
Breast cancer remains a significant global health challenge, with prognosis and treatment decisions largely dependent on clinical characteristics. Accurate prediction of patient outcomes is crucial for personalized treatment strategies. This study em
Externí odkaz:
http://arxiv.org/abs/2410.13404
Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classification. A sizable body of recen
Externí odkaz:
http://arxiv.org/abs/2410.09399
Autor:
He, Sizhuang, Levine, Daniel, Vrkic, Ivan, Bressana, Marco Francesco, Zhang, David, Rizvi, Syed Asad, Zhang, Yangtian, Zappala, Emanuele, van Dijk, David
We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables
Externí odkaz:
http://arxiv.org/abs/2410.05292
Autor:
Zhang, Shiyang, Patel, Aakash, Rizvi, Syed A, Liu, Nianchen, He, Sizhuang, Karbasi, Amin, Zappala, Emanuele, van Dijk, David
We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (
Externí odkaz:
http://arxiv.org/abs/2410.02536
Autor:
Rizvi, Syed Muhammad Aqdas
This literature review surveys the advancements of keyword spotting (KWS) technologies, specifically focusing on Urdu, Pakistan's low-resource language (LRL), which has complex phonetics. Despite the global strides in speech technology, Urdu presents
Externí odkaz:
http://arxiv.org/abs/2409.16317
Autor:
Khan, Abdul Hannan, Rizvi, Syed Tahseen Raza, Macharavtu, Dheeraj Varma Chittari, Dengel, Andreas
Autonomous driving systems require a quick and robust perception of the nearby environment to carry out their routines effectively. With the aim to avoid collisions and drive safely, autonomous driving systems rely heavily on object detection. Howeve
Externí odkaz:
http://arxiv.org/abs/2405.07698
With recent advances in computer vision, it appears that autonomous driving will be part of modern society sooner rather than later. However, there are still a significant number of concerns to address. Although modern computer vision techniques demo
Externí odkaz:
http://arxiv.org/abs/2402.00128
Deep neural networks, despite their success in numerous applications, often function without established theoretical foundations. In this paper, we bridge this gap by drawing parallels between deep learning and classical numerical analysis. By framin
Externí odkaz:
http://arxiv.org/abs/2310.01618
Publikováno v:
Volume 3: ICAART, 2023, pages - 550-557
The rapid advancement in deep learning over the past decade has transformed Facial Expression Recognition (FER) systems, as newer methods have been proposed that outperform the existing traditional handcrafted techniques. However, such a supervised l
Externí odkaz:
http://arxiv.org/abs/2310.00287