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pro vyhledávání: '"Welch William J"'
Dimensional analysis (DA) pays attention to fundamental physical dimensions such as length and mass when modelling scientific and engineering systems. It goes back at least a century to Buckingham's Pi theorem, which characterizes a scientifically me
Externí odkaz:
http://arxiv.org/abs/2312.10100
Autor:
Schrunner, Stefan, Pishrobat, Parham, Janssen, Joseph, Jenul, Anna, Cao, Jiguo, Ameli, Ali A., Welch, William J.
Statistical models are an essential tool to model, forecast and understand the hydrological processes in watersheds. In particular, the understanding of time lags associated with the delay between rainfall occurrence and subsequent changes in streamf
Externí odkaz:
http://arxiv.org/abs/2306.00453
Autor:
Janssen, Joseph, Meng, Shizhe, Haris, Asad, Schrunner, Stefan, Cao, Jiguo, Welch, William J., Kunz, Nadja, Ameli, Ali A.
Scientists and statisticians often want to learn about the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models confirms that they are promising for this purpose, but several notable
Externí odkaz:
http://arxiv.org/abs/2303.06501
Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher. However, applying KD in image regression with a scalar respon
Externí odkaz:
http://arxiv.org/abs/2104.03164
Recently, subsampling or refining images generated from unconditional GANs has been actively studied to improve the overall image quality. Unfortunately, these methods are often observed less effective or inefficient in handling conditional GANs (cGA
Externí odkaz:
http://arxiv.org/abs/2103.11166
This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainl
Externí odkaz:
http://arxiv.org/abs/2011.07466
Publikováno v:
The CAAI International Conference on Artificial Intelligence (CICAI 2021)
Modern methods often formulate the counting of cells from microscopic images as a regression problem and more or less rely on expensive, manually annotated training images (e.g., dot annotations indicating the centroids of cells or segmentation masks
Externí odkaz:
http://arxiv.org/abs/2010.14782
Publikováno v:
BMC Bioinformatics, Vol 7, Iss 1, p 521 (2006)
Abstract Background Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide – adenine (A), thymine (T), cytosine (C) or guanine (G) – is altered. Arguably, SNPs account for more than 90% of human gen
Externí odkaz:
https://doaj.org/article/5c9183bfe2df4cebbed0d3ea1f31b404
Filtering out unrealistic images from trained generative adversarial networks (GANs) has attracted considerable attention recently. Two density ratio based subsampling methods---Discriminator Rejection Sampling (DRS) and Metropolis-Hastings GAN (MH-G
Externí odkaz:
http://arxiv.org/abs/1909.10670