Zobrazeno 1 - 10
of 38 797
pro vyhledávání: '"A, Ikram"'
Autor:
Ahmad, Murad, Ali, Liaqat, Imran, Muhammad, Rameez-ul-Islam, Ikram, Manzoor, Din, Rafi Ud, Ahmad, Ashfaq, Ahmad, Iftikhar
Hyperentangled states are highly efficient and resource economical. This is because they enhance the quantum information encoding capabilities due to the correlated engagement of more than one degree of freedom of the same quantum entity while keepin
Externí odkaz:
http://arxiv.org/abs/2408.16397
Autor:
Salman, Muhammad, Zhao, Benjamin Zi Hao, Asghar, Hassan Jameel, Ikram, Muhammad, Kaushik, Sidharth, Kaafar, Mohamed Ali
Adversarial examples add imperceptible alterations to inputs with the objective to induce misclassification in machine learning models. They have been demonstrated to pose significant challenges in domains like image classification, with results show
Externí odkaz:
http://arxiv.org/abs/2408.02310
Autor:
Qayyum, Hina, Ikram, Muhammad, Zhao, Benjamin, Wood, Ian, Kaafar, Mohamad Ali, Kourtellis, Nicolas
Toxic sentiment analysis on Twitter (X) often focuses on specific topics and events such as politics and elections. Datasets of toxic users in such research are typically gathered through lexicon-based techniques, providing only a cross-sectional vie
Externí odkaz:
http://arxiv.org/abs/2406.02801
OpenAI's ChatGPT initiated a wave of technical iterations in the space of Large Language Models (LLMs) by demonstrating the capability and disruptive power of LLMs. OpenAI has prompted large organizations to respond with their own advancements and mo
Externí odkaz:
http://arxiv.org/abs/2405.10547
Publikováno v:
Braz J Phys 54, 105 (2024)
The present study focuses on investigating the shape evolution of neutron-rich even-even Osmium (Os) transitional nuclei within the range of neutron number N = 82 to N = 190. The investigation is conducted using density-dependent meson-nucleon and po
Externí odkaz:
http://arxiv.org/abs/2405.09085
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
Autor:
Zeghlache, Rachid, Conze, Pierre-Henri, Daho, Mostafa El Habib, Li, Yihao, Boité, Hugo Le, Tadayoni, Ramin, Massin, Pascal, Cochener, Béatrice, Rezaei, Alireza, Brahim, Ikram, Quellec, Gwenolé, Lamard, Mathieu
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE). We introduce a "time-aware head" in a framework trained through self-supervised learning (SSL) to leverage temporal
Externí odkaz:
http://arxiv.org/abs/2404.07091
Autor:
Zeghlache, Rachid, Conze, Pierre-Henri, Daho, Mostafa El Habib, Li, Yihao, Rezaei, Alireza, Boité, Hugo Le, Tadayoni, Ramin, Massin, Pascal, Cochener, Béatrice, Brahim, Ikram, Quellec, Gwenolé, Lamard, Mathieu
Pre-training strategies based on self-supervised learning (SSL) have proven to be effective pretext tasks for many downstream tasks in computer vision. Due to the significant disparity between medical and natural images, the application of typical SS
Externí odkaz:
http://arxiv.org/abs/2403.16272
Publikováno v:
IEEE International Conference on Mathematics and Computers in Sciences and Industry (CMCSI 23), IEEE, Oct 2023, Ath{\`e}nes, Greece
Pairwise Markov Models (PMMs) extend the wellknown Hidden Markov Models (HMMs). Being significantly more general, PMMs enable several types of processing, like Bayesian filtering or smoothing, similar to those used in HMMs. In this paper, we deal wit
Externí odkaz:
http://arxiv.org/abs/2402.07532
Autor:
Akhtar, Mohammad Majid, Bhuiyan, Navid Shadman, Masood, Rahat, Ikram, Muhammad, Kanhere, Salil S.
The detection of automated accounts, also known as "social bots", has been an increasingly important concern for online social networks (OSNs). While several methods have been proposed for detecting social bots, significant research gaps remain. Firs
Externí odkaz:
http://arxiv.org/abs/2402.03740
Generative Flow Networks (GFlowNets) are a family of probabilistic generative models that learn to sample compositional objects proportional to their rewards. One big challenge of GFlowNets is training them effectively when dealing with long time hor
Externí odkaz:
http://arxiv.org/abs/2402.02186