Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Gyawali, Prashnna K."'
Autor:
Rahman, Chowdhury Mohammad Abid, Bhandari, Ghadendra, Nasrabadi, Nasser M, Romero, Aldo H., Gyawali, Prashnna K.
Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced te
Externí odkaz:
http://arxiv.org/abs/2407.18847
Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads to memory and behavior impairment severe enough to interfere with daily life activities. Understanding this disease pathogenesis can drive the development of new targets an
Externí odkaz:
http://arxiv.org/abs/2405.17433
Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of interest with deformable transformations can help mitigate this challen
Externí odkaz:
http://arxiv.org/abs/2307.13645
Autor:
Regmi, Sudarshan, Panthi, Bibek, Dotel, Sakar, Gyawali, Prashnna K., Stoyanov, Danail, Bhattarai, Binod
Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature,
Externí odkaz:
http://arxiv.org/abs/2305.17797
Autor:
Toloubidokhti, Maryam, Kumar, Nilesh, Li, Zhiyuan, Gyawali, Prashnna K., Zenger, Brian, Good, Wilson W., MacLeod, Rob S., Wang, Linwei
Publikováno v:
The 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions. I
Externí odkaz:
http://arxiv.org/abs/2211.01373
With the growing adoption of deep learning models in different real-world domains, including computational biology, it is often necessary to understand which data features are essential for the model's decision. Despite extensive recent efforts to de
Externí odkaz:
http://arxiv.org/abs/2210.00604
Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European) ancestry. This can lead to biases in the risk p
Externí odkaz:
http://arxiv.org/abs/2205.04673
Recent advances in appearance-based models have shown improved eye tracking performance in difficult scenarios like occlusion due to eyelashes, eyelids or camera placement, and environmental reflections on the cornea and glasses. The key reason for t
Externí odkaz:
http://arxiv.org/abs/2103.09369
Several scalable sample-based methods to compute the Kullback Leibler (KL) divergence between two distributions have been proposed and applied in large-scale machine learning models. While they have been found to be unstable, the theoretical root cau
Externí odkaz:
http://arxiv.org/abs/2002.11187
Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations. However, they produce blurry images due to a lack of explicit emphasis over hig
Externí odkaz:
http://arxiv.org/abs/1911.05627