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pro vyhledávání: '"Akhtar Naveed"'
The widespread availability of multimodal generative models has sparked critical discussions on their fairness, reliability, and potential for misuse. While text-to-image models can produce high-fidelity, user-guided images, they also exhibit unpredi
Externí odkaz:
http://arxiv.org/abs/2411.13981
Training multimodal generative models on large, uncurated datasets can result in users being exposed to harmful, unsafe and controversial or culturally-inappropriate outputs. While model editing has been proposed to remove or filter undesirable conce
Externí odkaz:
http://arxiv.org/abs/2411.13982
Image restoration and spectral reconstruction are longstanding computer vision tasks. Currently, CNN-transformer hybrid models provide state-of-the-art performance for these tasks. The key common ingredient in the architectural designs of these model
Externí odkaz:
http://arxiv.org/abs/2410.00380
Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input. Within our approach, robust featur
Externí odkaz:
http://arxiv.org/abs/2407.04370
Autor:
Azam, Basim, Akhtar, Naveed
Kolmogorov-Arnold Networks (KANs) introduce a paradigm of neural modeling that implements learnable functions on the edges of the networks, diverging from the traditional node-centric activations in neural networks. This work assesses the applicabili
Externí odkaz:
http://arxiv.org/abs/2406.09087
Due to its widespread applications, human action recognition is one of the most widely studied research problems in Computer Vision. Recent studies have shown that addressing it using multimodal data leads to superior performance as compared to relyi
Externí odkaz:
http://arxiv.org/abs/2405.15813
Attribution methods compute importance scores for input features to explain the output predictions of deep models. However, accurate assessment of attribution methods is challenged by the lack of benchmark fidelity for attributing model predictions.
Externí odkaz:
http://arxiv.org/abs/2405.02344
Text-to-image (T2I) generative models have gained increased popularity in the public domain. While boasting impressive user-guided generative abilities, their black-box nature exposes users to intentionally- and intrinsically-biased outputs. Bias man
Externí odkaz:
http://arxiv.org/abs/2404.02530
Publikováno v:
Saudi Pharmaceutical Journal, Vol 26, Iss 8, Pp 1170-1177 (2018)
Focus of the study was to design a novel and cost effective extraction technique for the lycopene from Lycopersicum esculentum L. fruit and to develop and characterize a stable emulgel formulation containing lycopene as an active ingredient as well a
Externí odkaz:
https://doaj.org/article/9157761c69db431d95cb49df0ce12616
Autor:
Hameed Abdul, Akhtar Naveed
Publikováno v:
Acta Pharmaceutica, Vol 68, Iss 1, Pp 47-60 (2018)
In the present study, berries of two different species of Solanaceae family, Withania somnifera (WS) and Solanum nigrum (SN), were extracted in methanol and then fractionated with solvents, ranging from non-polar to polar, for their phytochemical pro
Externí odkaz:
https://doaj.org/article/5ef36ce321e84152820a873dc2cf1778