Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Malof Jordan"'
Publikováno v:
Nanophotonics, Vol 13, Iss 13, Pp 2323-2334 (2024)
We demonstrate transfer learning as a tool to improve the efficacy of training deep learning models based on residual neural networks (ResNets). Specifically, we examine its use for study of multi-scale electrically large metasurface arrays under ope
Externí odkaz:
https://doaj.org/article/bca98c5481a04b66b65d7344acb91bc2
Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion, i.e., conditions in which objects are partially covered from the view o
Externí odkaz:
http://arxiv.org/abs/2409.10775
Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable from huma
Externí odkaz:
http://arxiv.org/abs/2404.15458
In this study, we address the challenge of obtaining a Green's function operator for linear partial differential equations (PDEs). The Green's function is well-sought after due to its ability to directly map inputs to solutions, bypassing the need fo
Externí odkaz:
http://arxiv.org/abs/2306.02925
Autor:
Li, Wenhao, Sedeh, Hooman Barati, Padilla, Willie J., Ren, Simiao, Malof, Jordan, Litchinitser, Natalia M.
Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force optimization method
Externí odkaz:
http://arxiv.org/abs/2305.18589
Autor:
Ren, Simiao, Luzi, Francesco, Lahrichi, Saad, Kassaw, Kaleb, Collins, Leslie M., Bradbury, Kyle, Malof, Jordan M.
Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a
Externí odkaz:
http://arxiv.org/abs/2304.13000
Deep active learning (DAL) methods have shown significant improvements in sample efficiency compared to simple random sampling. While these studies are valuable, they nearly always assume that optimal DAL hyperparameter (HP) settings are known in adv
Externí odkaz:
http://arxiv.org/abs/2302.00098
Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly available datasets that can be used for such training are limited in their si
Externí odkaz:
http://arxiv.org/abs/2212.12824
We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natural system that produce a given set of observed measurements. Recent wor
Externí odkaz:
http://arxiv.org/abs/2211.14366
There is evidence that transformers offer state-of-the-art recognition performance on tasks involving overhead imagery (e.g., satellite imagery). However, it is difficult to make unbiased empirical comparisons between competing deep learning models,
Externí odkaz:
http://arxiv.org/abs/2210.12599