Zobrazeno 1 - 10
of 239
pro vyhledávání: '"CHEN Shiyang"'
Publikováno v:
Acta Biochimica et Biophysica Sinica, Vol 56, Pp 1573-1583 (2024)
Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease and poses a substantial health burden with increasing incidence globally. NAFLD encompasses a spectrum extending from hepatic steatosis to nonalcoholic steatohepatit
Externí odkaz:
https://doaj.org/article/1c2353b4aafb4b68af0575b7bf555b75
Autor:
Coalson, Zachary, Woo, Jeonghyun, Chen, Shiyang, Sun, Yu, Yang, Lishan, Nair, Prashant, Fang, Bo, Hong, Sanghyun
We introduce a new class of attacks on commercial-scale (human-aligned) language models that induce jailbreaking through targeted bitwise corruptions in model parameters. Our adversary can jailbreak billion-parameter language models with fewer than 2
Externí odkaz:
http://arxiv.org/abs/2412.07192
Publikováno v:
Leida xuebao, Vol 8, Iss 4, Pp 527-536 (2019)
This paper focuses on an improved imaging-algorithm for the spotlight Synthetic Aperture Radar (spotlight SAR) with continuous Pulse Repetition Frequency (PRF) variation in extremely high-resolution imaging-process. PRI variation is conventionally em
Externí odkaz:
https://doaj.org/article/e8b4ba3053ec4cfab1cb6ca8c0780258
The wide application of machine learning (ML) techniques in statistics physics has presented new avenues for research in this field. In this paper, we introduce a semi-supervised learning method based on Siamese Neural Networks (SNN), trying to explo
Externí odkaz:
http://arxiv.org/abs/2405.16769
Autor:
Xia, Haojun, Zheng, Zhen, Wu, Xiaoxia, Chen, Shiyang, Yao, Zhewei, Youn, Stephen, Bakhtiari, Arash, Wyatt, Michael, Zhuang, Donglin, Zhou, Zhongzhu, Ruwase, Olatunji, He, Yuxiong, Song, Shuaiwen Leon
Six-bit quantization (FP6) can effectively reduce the size of large language models (LLMs) and preserve the model quality consistently across varied applications. However, existing systems do not provide Tensor Core support for FP6 quantization and s
Externí odkaz:
http://arxiv.org/abs/2401.14112
Autor:
Wu, Xiaoxia, Xia, Haojun, Youn, Stephen, Zheng, Zhen, Chen, Shiyang, Bakhtiari, Arash, Wyatt, Michael, Aminabadi, Reza Yazdani, He, Yuxiong, Ruwase, Olatunji, Song, Leon, Yao, Zhewei
This study examines 4-bit quantization methods like GPTQ in large language models (LLMs), highlighting GPTQ's overfitting and limited enhancement in Zero-Shot tasks. While prior works merely focusing on zero-shot measurement, we extend task scope to
Externí odkaz:
http://arxiv.org/abs/2312.08583
Autor:
Chen, Xiangna, Liu, Feiyi, Deng, Weibing, Chen, Shiyang, Shen, Jianmin, Papp, Gabor, Li, Wei, Yang, Chunbin
Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional $q$-state Potts model on cubic
Externí odkaz:
http://arxiv.org/abs/2312.02479
The percolation study offers valuable insights into the characteristics of phase transition, shedding light on the underlying mechanisms that govern the formation of global connectivity within the system. We explore the percolation phase transition i
Externí odkaz:
http://arxiv.org/abs/2311.14245
Publikováno v:
Jixie chuandong, Vol 40, Pp 10-14 (2016)
Taking a centralized drive electric vehicle power train as study object,considering the influence of coolant and lubricating oil,the fluid- structure model is established. The modal and vibration response of the model is researched. First,the fluid-
Externí odkaz:
https://doaj.org/article/58aaff14f50c4d3580bd19d8f851ae11
Autor:
Song, Shuaiwen Leon, Kruft, Bonnie, Zhang, Minjia, Li, Conglong, Chen, Shiyang, Zhang, Chengming, Tanaka, Masahiro, Wu, Xiaoxia, Rasley, Jeff, Awan, Ammar Ahmad, Holmes, Connor, Cai, Martin, Ghanem, Adam, Zhou, Zhongzhu, He, Yuxiong, Luferenko, Pete, Kumar, Divya, Weyn, Jonathan, Zhang, Ruixiong, Klocek, Sylwester, Vragov, Volodymyr, AlQuraishi, Mohammed, Ahdritz, Gustaf, Floristean, Christina, Negri, Cristina, Kotamarthi, Rao, Vishwanath, Venkatram, Ramanathan, Arvind, Foreman, Sam, Hippe, Kyle, Arcomano, Troy, Maulik, Romit, Zvyagin, Maxim, Brace, Alexander, Zhang, Bin, Bohorquez, Cindy Orozco, Clyde, Austin, Kale, Bharat, Perez-Rivera, Danilo, Ma, Heng, Mann, Carla M., Irvin, Michael, Pauloski, J. Gregory, Ward, Logan, Hayot, Valerie, Emani, Murali, Xie, Zhen, Lin, Diangen, Shukla, Maulik, Foster, Ian, Davis, James J., Papka, Michael E., Brettin, Thomas, Balaprakash, Prasanna, Tourassi, Gina, Gounley, John, Hanson, Heidi, Potok, Thomas E, Pasini, Massimiliano Lupo, Evans, Kate, Lu, Dan, Lunga, Dalton, Yin, Junqi, Dash, Sajal, Wang, Feiyi, Shankar, Mallikarjun, Lyngaas, Isaac, Wang, Xiao, Cong, Guojing, Zhang, Pei, Fan, Ming, Liu, Siyan, Hoisie, Adolfy, Yoo, Shinjae, Ren, Yihui, Tang, William, Felker, Kyle, Svyatkovskiy, Alexey, Liu, Hang, Aji, Ashwin, Dalton, Angela, Schulte, Michael, Schulz, Karl, Deng, Yuntian, Nie, Weili, Romero, Josh, Dallago, Christian, Vahdat, Arash, Xiao, Chaowei, Gibbs, Thomas, Anandkumar, Anima, Stevens, Rick
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors fro
Externí odkaz:
http://arxiv.org/abs/2310.04610