Zobrazeno 1 - 10
of 13 267
pro vyhledávání: '"Khan, Mohammad A."'
Autor:
Khan, Mohammad Sadil, Sinha, Sankalp, Sheikh, Talha Uddin, Stricker, Didier, Ali, Sk Aziz, Afzal, Muhammad Zeshan
Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework fo
Externí odkaz:
http://arxiv.org/abs/2409.17106
In machining process, 3D reverse engineering of the mechanical system is an integral, highly important, and yet time consuming step to obtain parametric CAD models from 3D scans. Therefore, deep learning-based Scan-to-CAD modeling can offer designers
Externí odkaz:
http://arxiv.org/abs/2409.14087
Autor:
Ahmed, Istiak, Hossain, Md. Tanzim, Nahid, Md. Zahirul Islam, Sanjid, Kazi Shahriar, Junayed, Md. Shakib Shahariar, Uddin, M. Monir, Khan, Mohammad Monirujjaman
This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low ba
Externí odkaz:
http://arxiv.org/abs/2409.06018
As the field of artificial intelligence progresses, assistive technologies are becoming more widely used across all industries. The healthcare industry is no different, with numerous studies being done to develop assistive tools for healthcare profes
Externí odkaz:
http://arxiv.org/abs/2408.15827
Autor:
Speicher, Till, Khan, Mohammad Aflah, Wu, Qinyuan, Nanda, Vedant, Das, Soumi, Ghosh, Bishwamittra, Gummadi, Krishna P., Terzi, Evimaria
Understanding whether and to what extent large language models (LLMs) have memorised training data has important implications for the reliability of their output and the privacy of their training data. In order to cleanly measure and disentangle memo
Externí odkaz:
http://arxiv.org/abs/2407.19262
Autor:
Laskar, Md Tahmid Rahman, Alqahtani, Sawsan, Bari, M Saiful, Rahman, Mizanur, Khan, Mohammad Abdullah Matin, Khan, Haidar, Jahan, Israt, Bhuiyan, Amran, Tan, Chee Wei, Parvez, Md Rizwan, Hoque, Enamul, Joty, Shafiq, Huang, Jimmy
Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-w
Externí odkaz:
http://arxiv.org/abs/2407.04069
Autor:
Prashanth, USVSN Sai, Deng, Alvin, O'Brien, Kyle, S V, Jyothir, Khan, Mohammad Aflah, Borkar, Jaydeep, Choquette-Choo, Christopher A., Fuehne, Jacob Ray, Biderman, Stella, Ke, Tracy, Lee, Katherine, Saphra, Naomi
Memorization in language models is typically treated as a homogenous phenomenon, neglecting the specifics of the memorized data. We instead model memorization as the effect of a set of complex factors that describe each sample and relate it to the mo
Externí odkaz:
http://arxiv.org/abs/2406.17746
Autor:
Khan, Mohammad Nur Hossain, Li, Jialu, McElwain, Nancy L., Hasegawa-Johnson, Mark, Islam, Bashima
Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data c
Externí odkaz:
http://arxiv.org/abs/2406.17190
Integrating inertial measurement units (IMUs) with large language models (LLMs) advances multimodal AI by enhancing human activity understanding. We introduce SensorCaps, a dataset of 26,288 IMU-derived activity narrations, and OpenSQA, an instructio
Externí odkaz:
http://arxiv.org/abs/2406.14498
In Multi-agent Reinforcement Learning (MARL), accurately perceiving opponents' strategies is essential for both cooperative and adversarial contexts, particularly within dynamic environments. While Proximal Policy Optimization (PPO) and related algor
Externí odkaz:
http://arxiv.org/abs/2406.06500