Zobrazeno 1 - 10
of 1 223 721
pro vyhledávání: '"A, Muhammad"'
The Dilemma of Random Parameter Initialization and Barren Plateaus in Variational Quantum Algorithms
Autor:
Kashif, Muhammad, Shafique, Muhammad
This paper presents an easy-to-implement approach to mitigate the challenges posed by barren plateaus (BPs) in randomly initialized parameterized quantum circuits (PQCs) within variational quantum algorithms (VQAs). Recent state-of-the-art research i
Externí odkaz:
http://arxiv.org/abs/2412.06462
Hybrid Quantum Neural Networks (HQNNs) have gained attention for their potential to enhance computational performance by incorporating quantum layers into classical neural network (NN) architectures. However, a key question remains: Do quantum layers
Externí odkaz:
http://arxiv.org/abs/2412.04991
The rapid advancement in Quantum Computing (QC), particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML) applications. Despite their potential, fully-quantum QML algorit
Externí odkaz:
http://arxiv.org/abs/2412.04844
Autor:
Manan, Malik Abdul, Jinchao, Feng, Yaqub, Muhammad, Ahmed, Shahzad, Imran, Syed Muhammad Ali, Chuhan, Imran Shabir, Khan, Haroon Ahmed
Publikováno v:
Alexandria Engineering Journal Volume 105, October 2024, Pages 341-359
Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded C
Externí odkaz:
http://arxiv.org/abs/2412.02443
Autor:
Shaikh, Abdurrahman Javid, Abro, Abdul Ghani, Baig, Mirza Muhammad Ali, Siddiqui, Muhammad Adeel Ahmad, Abbas, Syed Mohsin
Full-vectorial finite difference method with perfectly matched layers boundaries is used to identify the single mode operation region of submicron rib waveguides fabricated using sili-con-on-insulator material system. Achieving high mode power confin
Externí odkaz:
http://arxiv.org/abs/2412.01236
Autor:
Ramzan, Muhammad Umer, Khaddim, Wahab, Rana, Muhammad Ehsan, Ali, Usman, Ali, Manohar, Hassan, Fiaz ul, Mehmood, Fatima
This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become
Externí odkaz:
http://arxiv.org/abs/2411.19690
Autor:
Danish, Muhammad Sohail, Munir, Muhammad Akhtar, Shah, Syed Roshaan Ali, Kuckreja, Kartik, Khan, Fahad Shahbaz, Fraccaro, Paolo, Lacoste, Alexandre, Khan, Salman
While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they fall short in addressing the unique demands of geospatial applications. Generic VLM benchmarks are not designed to handle the complexities of geospatial
Externí odkaz:
http://arxiv.org/abs/2411.19325
Autor:
Sinha, Sankalp, Khan, Mohammad Sadil, Usama, Muhammad, Sam, Shino, Stricker, Didier, Ali, Sk Aziz, Afzal, Muhammad Zeshan
Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive datase
Externí odkaz:
http://arxiv.org/abs/2411.17945
Autor:
Vayani, Ashmal, Dissanayake, Dinura, Watawana, Hasindri, Ahsan, Noor, Sasikumar, Nevasini, Thawakar, Omkar, Ademtew, Henok Biadglign, Hmaiti, Yahya, Kumar, Amandeep, Kuckreja, Kartik, Maslych, Mykola, Ghallabi, Wafa Al, Mihaylov, Mihail, Qin, Chao, Shaker, Abdelrahman M, Zhang, Mike, Ihsani, Mahardika Krisna, Esplana, Amiel, Gokani, Monil, Mirkin, Shachar, Singh, Harsh, Srivastava, Ashay, Hamerlik, Endre, Izzati, Fathinah Asma, Maani, Fadillah Adamsyah, Cavada, Sebastian, Chim, Jenny, Gupta, Rohit, Manjunath, Sanjay, Zhumakhanova, Kamila, Rabevohitra, Feno Heriniaina, Amirudin, Azril, Ridzuan, Muhammad, Kareem, Daniya, More, Ketan, Li, Kunyang, Shakya, Pramesh, Saad, Muhammad, Ghasemaghaei, Amirpouya, Djanibekov, Amirbek, Azizov, Dilshod, Jankovic, Branislava, Bhatia, Naman, Cabrera, Alvaro, Obando-Ceron, Johan, Otieno, Olympiah, Farestam, Fabian, Rabbani, Muztoba, Baliah, Sanoojan, Sanjeev, Santosh, Shtanchaev, Abduragim, Fatima, Maheen, Nguyen, Thao, Kareem, Amrin, Aremu, Toluwani, Xavier, Nathan, Bhatkal, Amit, Toyin, Hawau, Chadha, Aman, Cholakkal, Hisham, Anwer, Rao Muhammad, Felsberg, Michael, Laaksonen, Jorma, Solorio, Thamar, Choudhury, Monojit, Laptev, Ivan, Shah, Mubarak, Khan, Salman, Khan, Fahad
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource
Externí odkaz:
http://arxiv.org/abs/2411.16508
Autor:
Akhlaq, Filza, Arshad, Alina, Hayati, Muhammad Yehya, Shamsi, Jawwad A., Khan, Muhammad Burhan
Detecting mixed-critical events through computer vision is challenging due to the need for contextual understanding to assess event criticality accurately. Mixed critical events, such as fires of varying severity or traffic incidents, demand adaptabl
Externí odkaz:
http://arxiv.org/abs/2411.15773