Zobrazeno 1 - 10
of 768
pro vyhledávání: '"KUMAR, NILESH"'
Autor:
Lovering, Charles, Krumdick, Michael, Lai, Viet Dac, Kumar, Nilesh, Reddy, Varshini, Koncel-Kedziorski, Rik, Tanner, Chris
Some information is factual (e.g., "Paris is in France"), whereas other information is probabilistic (e.g., "the coin flip will be a [Heads/Tails]."). We believe that good Language Models (LMs) should understand and reflect this nuance. Our work inve
Externí odkaz:
http://arxiv.org/abs/2410.16007
Publikováno v:
J. Chem. Phys. 160, 124707 (2024)
Semiconducting MXenes are an intriguing two-dimensional (2D) material class with promising electronic and optoelectronic properties. Here, we focused on recently prepared Hf-based MXenes, namely Hf$_3$C$_2$O$_2$ and Hf$_2$CO$_2$. Using the first-prin
Externí odkaz:
http://arxiv.org/abs/2404.01797
Autor:
Kumar, Nilesh, Kumar, Jatinder
Publikováno v:
Aircraft Engineering and Aerospace Technology, 2024, Vol. 96, Issue 9, pp. 1234-1246.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/AEAT-03-2024-0069
Spurious correlation caused by subgroup underrepresentation has received increasing attention as a source of bias that can be perpetuated by deep neural networks (DNNs). Distributionally robust optimization has shown success in addressing this bias,
Externí odkaz:
http://arxiv.org/abs/2308.06434
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
Publikováno v:
Leadership & Organization Development Journal, 2024, Vol. 45, Issue 6, pp. 1011-1027.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/LODJ-09-2023-0501
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