Zobrazeno 1 - 10
of 64 865
pro vyhledávání: '"Hameed, A."'
Model training requires significantly more memory, compared with inference. Parameter efficient fine-tuning (PEFT) methods provide a means of adapting large models to downstream tasks using less memory. However, existing methods such as adapters, pro
Externí odkaz:
http://arxiv.org/abs/2407.07802
In this paper, we compare general-purpose pretrained models, GPT-4-Turbo and Llama-3-8b-Instruct with special-purpose models fine-tuned on specific tasks, XLM-Roberta-large, mT5-large, and Llama-3-8b-Instruct. We focus on seven classification and six
Externí odkaz:
http://arxiv.org/abs/2407.04459
Autor:
Awais, Muhammad, Hameed, Mehaboobathunnisa Sahul, Bhattacharya, Bidisha, Reiner, Orly, Anwer, Rao Muhammad
Recent advances have enabled the study of human brain development using brain organoids derived from stem cells. Quantifying cellular processes like mitosis in these organoids offers insights into neurodevelopmental disorders, but the manual analysis
Externí odkaz:
http://arxiv.org/abs/2406.19556
Second-order Recurrent Neural Networks (2RNNs) extend RNNs by leveraging second-order interactions for sequence modelling. These models are provably more expressive than their first-order counterparts and have connections to well-studied models from
Externí odkaz:
http://arxiv.org/abs/2406.05045
We have investigated the single and double diffractive production of dileptons and photons in ultra-peripheral collisions at the Large Hadron Collider (LHC). Utilizing advanced theoretical models that integrate quantum electrodynamics (QED) and Quant
Externí odkaz:
http://arxiv.org/abs/2405.16242
The Hardware Trojan (HT) problem can be thought of as a continuous game between attackers and defenders, each striving to outsmart the other by leveraging any available means for an advantage. Machine Learning (ML) has recently been key in advancing
Externí odkaz:
http://arxiv.org/abs/2405.15184
Autor:
Khayr, Issam, Hameed, Sajna, Budić, Jakov, He, Xing, Spieker, Richard, Najev, Ana, Zhao, Zinan, Yue, Li, Krogstad, Matthew, Ye, Feng, Liu, Yaohua, Osborn, Raymond, Rosenkranz, Stephan, Li, Yuan, Pelc, Damjan, Greven, Martin
Dislocation engineering has the potential to open new avenues toward the exploration and modification of the properties of quantum materials. Strontium titanate (SrTiO3, STO) and potassium tantalate (KTaO3, KTO) are incipient ferroelectrics that show
Externí odkaz:
http://arxiv.org/abs/2405.13249
Autor:
Qazzaz, Mohammed M. H., Zaidi, Syed A. R., McLernon, Desmond C., Salama, Abdelaziz, Al-Hameed, Aubida A.
Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a
Externí odkaz:
http://arxiv.org/abs/2405.10042
Autor:
Aktar, Shamminuj, Bärtschi, Andreas, Oyen, Diane, Eidenbenz, Stephan, Badawy, Abdel-Hameed A.
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full pot
Externí odkaz:
http://arxiv.org/abs/2405.08100
Autor:
Khan, Awais Hameed, Kegalle, Hiruni, D'Silva, Rhea, Watt, Ned, Whelan-Shamy, Daniel, Ghahremanlou, Lida, Magee, Liam
Large Language Models (LLMs) are promising analytical tools. They can augment human epistemic, cognitive and reasoning abilities, and support 'sensemaking', making sense of a complex environment or subject by analysing large volumes of data with a se
Externí odkaz:
http://arxiv.org/abs/2405.06919