Zobrazeno 1 - 10
of 1 494
pro vyhledávání: '"AHMAD, NASIR"'
Autor:
Koenders, Kees, Schnitzpan, Leo, Kammerbauer, Fabian, Shu, Sinan, Jakob, Gerhard, Kläui, Mathis, Mentink, Johan, Ahmad, Nasir, van Gerven, Marcel
Brain-inspired learning in physical hardware has enormous potential to learn fast at minimal energy expenditure. One of the characteristics of biological learning systems is their ability to learn in the presence of various noise sources. Inspired by
Externí odkaz:
http://arxiv.org/abs/2412.12783
In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable citie
Externí odkaz:
http://arxiv.org/abs/2412.03600
Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient descent for learning, the need for exact gradients and complex informa
Externí odkaz:
http://arxiv.org/abs/2410.13563
Autor:
Ahmad, Nasir
Herein the topics of (natural) gradient descent, data decorrelation, and approximate methods for backpropagation are brought into a common discussion. Natural gradient descent illuminates how gradient vectors, pointing at directions of steepest desce
Externí odkaz:
http://arxiv.org/abs/2407.10780
Efficiency of neural network inference is undeniably important in a time where commercial use of AI models increases daily. Node pruning is the art of removing computational units such as neurons, filters, attention heads, or even entire layers to si
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