Zobrazeno 1 - 10
of 1 898
pro vyhledávání: '"Ahmad Nasir"'
Autor:
Ahmad, Nasir
Herein the topics of (natural) gradient descent, data decorrelation, and approximate methods for backpropagation are brought into a dialogue. Natural gradient descent illuminates how gradient vectors, pointing at directions of steepest descent, can b
Externí odkaz:
http://arxiv.org/abs/2407.10780
A significant increase in the commercial use of deep neural network models increases the need for efficient AI. Node pruning is the art of removing computational units such as neurons, filters, attention heads, or even entire layers while keeping net
Externí odkaz:
http://arxiv.org/abs/2405.17506
The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a significant computational cost and therefore a high carbon footprint. Convergi
Externí odkaz:
http://arxiv.org/abs/2405.02385
Over the last decade, similar to other application domains, social media content has been proven very effective in disaster informatics. However, due to the unstructured nature of the data, several challenges are associated with disaster analysis in
Externí odkaz:
http://arxiv.org/abs/2405.00903
Autor:
Auyb, Muhammad Asif, Zamir, Muhammad Tayyab, Khan, Imran, Naseem, Hannia, Ahmad, Nasir, Ahmad, Kashif
This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top pr
Externí odkaz:
http://arxiv.org/abs/2404.14977
In recent years, the increasing use of Artificial Intelligence based text generation tools has posed new challenges in document provenance, authentication, and authorship detection. However, advancements in stylometry have provided opportunities for
Externí odkaz:
http://arxiv.org/abs/2401.06752
Backpropagation (BP) remains the dominant and most successful method for training parameters of deep neural network models. However, BP relies on two computationally distinct phases, does not provide a satisfactory explanation of biological learning,
Externí odkaz:
http://arxiv.org/abs/2310.00965
Autor:
Ahmad Nasir, Khan Khalid, Khan Sher Wali, Ur Rashid Haroon, Irum, Zahoor Muhammad, Umar Muhammad Naveed, Ullah Riaz, Ali Essam A.
Publikováno v:
Open Chemistry, Vol 22, Iss 1, Pp 1868-78 (2024)
Externí odkaz:
https://doaj.org/article/9b2d005c98024b4490489b48ca36abfc
Autor:
Zamir, Muhammad Tayyab, Ayub, Muhammad Asif, Khan, Jebran, Ikram, Muhammad Jawad, Ahmad, Nasir, Ahmad, Kashif
Publikováno v:
IEEE ICAISC 2023
Style analysis, which is relatively a less explored topic, enables several interesting applications. For instance, it allows authors to adjust their writing style to produce a more coherent document in collaboration. Similarly, style analysis can als
Externí odkaz:
http://arxiv.org/abs/2303.01197
Autor:
Suleman, Muhammad, Asif, Muhammad, Zamir, Tayyab, Mehmood, Ayaz, Khan, Jebran, Ahmad, Nasir, Ahmad, Kashif
This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask a
Externí odkaz:
http://arxiv.org/abs/2301.00321