Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Viren, Brett"'
Resampling is a common technique applied in digital signal processing. Based on the Fast Fourier Transformation (FFT), we apply an optimization called here the LMN method to achieve fast and robust re-sampling. In addition to performance comparisons
Externí odkaz:
http://arxiv.org/abs/2407.21750
Autor:
Go, Yeonju, Torbunov, Dmitrii, Rinn, Timothy, Huang, Yi, Yu, Haiwang, Viren, Brett, Lin, Meifeng, Ren, Yihui, Huang, Jin
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. Howe
Externí odkaz:
http://arxiv.org/abs/2406.01602
Autor:
Atif, Mohammad, Battacharya, Meghna, Calafiura, Paolo, Childers, Taylor, Dewing, Mark, Dong, Zhihua, Gutsche, Oliver, Habib, Salman, Knoepfel, Kyle, Kortelainen, Matti, Kwok, Ka Hei Martin, Leggett, Charles, Lin, Meifeng, Pascuzzi, Vincent, Strelchenko, Alexei, Tsulaia, Vakhtang, Viren, Brett, Wang, Tianle, Yeo, Beomki, Yu, Haiwang
High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership
Externí odkaz:
http://arxiv.org/abs/2306.15869
Autor:
Huang, Yi, Torbunov, Dmitrii, Viren, Brett, Yu, Haiwang, Huang, Jin, Lin, Meifeng, Ren, Yihui
Deep learning algorithms often are trained and deployed on different datasets. Any systematic difference between the training and a test dataset may degrade the algorithm performance--what is known as the domain shift problem. This issue is prevalent
Externí odkaz:
http://arxiv.org/abs/2304.12858
Autor:
Lin, Meifeng, Dong, Zhihua, Wang, Tianle, Atif, Mohammad, Battacharya, Meghna, Knoepfel, Kyle, Leggett, Charles, Viren, Brett, Yu, Haiwang
The evolution of the computing landscape has resulted in the proliferation of diverse hardware architectures, with different flavors of GPUs and other compute accelerators becoming more widely available. To facilitate the efficient use of these archi
Externí odkaz:
http://arxiv.org/abs/2304.01841
Autor:
Torbunov, Dmitrii, Huang, Yi, Tseng, Huan-Hsin, Yu, Haiwang, Huang, Jin, Yoo, Shinjae, Lin, Meifeng, Viren, Brett, Ren, Yihui
An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner. While initial solutions to the I2I problem were provided by generative adversarial neural networks (GANs), diff
Externí odkaz:
http://arxiv.org/abs/2303.16280
Autor:
Bhattacharya, Meghna, Calafiura, Paolo, Childers, Taylor, Dewing, Mark, Dong, Zhihua, Gutsche, Oliver, Habib, Salman, Ju, Xiangyang, Kirby, Michael, Knoepfel, Kyle, Kortelainen, Matti, Kwok, Martin, Leggett, Charles, Lin, Meifeng, Pascuzzi, Vincent R., Strelchenko, Alexei, Viren, Brett, Yeo, Beomki, Yu, Haiwang
Today's world of scientific software for High Energy Physics (HEP) is powered by x86 code, while the future will be much more reliant on accelerators like GPUs and FPGAs. The portable parallelization strategies (PPS) project of the High Energy Physic
Externí odkaz:
http://arxiv.org/abs/2203.09945
Autor:
Viren, Brett, Huang, Jin, Huang, Yi, Lin, Meifeng, Ren, Yihui, Terao, Kazuhiro, Torbunov, Dmitrii, Yu, Haiwang
Training an AI/ML system on simulated data while using that system to infer on data from real detectors introduces a systematic error which is difficult to estimate and in many analyses is simply not confronted. It is crucial to minimize and to quant
Externí odkaz:
http://arxiv.org/abs/2203.06112
Autor:
Fleming, Bonnie, Knoepfel, Kyle, Lin, Meifeng, Qian, Xin, Ren, Yihui, Viren, Brett, Wei, Hanyu, Yoo, Shinjae, Yu, Haiwang
DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by modern high-performance computing facilities. In order to efficiently e
Externí odkaz:
http://arxiv.org/abs/2203.06104
Autor:
Torbunov, Dmitrii, Huang, Yi, Yu, Haiwang, Huang, Jin, Yoo, Shinjae, Lin, Meifeng, Viren, Brett, Ren, Yihui
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN
Externí odkaz:
http://arxiv.org/abs/2203.02557