Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Haoran You"'
Autor:
Hanzhou Wu, Tao Jiang, Zhen Liu, Shifeng Fu, Jiawei Cheng, Haoran You, Jie Jiao, Mirza Bichurin, Oleg Sokolov, Sergey Ivanov, Yaojin Wang
Publikováno v:
Advanced Electronic Materials, Vol 9, Iss 7, Pp n/a-n/a (2023)
Abstract Wireless communication has always been an indispensable element in the modern information‐based society. Beyond the commercial electrical antenna, very low frequency (VLF) mechanical antennas have recently become research hotspot since the
Externí odkaz:
https://doaj.org/article/d2b300173cb847be877764e63c6225d8
Publikováno v:
IEEE Transactions on Circuits and Systems I: Regular Papers. 70:2523-2536
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7357f50b9eb8473f33b30cb3403f56fd
https://doi.org/10.1109/tcsi.2023.3256700
https://doi.org/10.1109/tcsi.2023.3256700
Autor:
Haoran You, Yang Zhao, Cheng Wan, Zhongzhi Yu, Yonggan Fu, Jiayi Yuan, Shang Wu, Shunyao Zhang, Yongan Zhang, Chaojian Li, Vivek Boominathan, Ashok Veeraraghavan, Ziyun Li, Yingyan Celine Lin
Publikováno v:
IEEE Micro. :1-9
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b77a59390d6bdda326df15d1c0d77d9
https://doi.org/10.1109/mm.2023.3274736
https://doi.org/10.1109/mm.2023.3274736
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:8910-8918
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. However, it remains notoriously challenging to train and inference GCNs over large graph datasets, limiting their appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cec943995d76a484c97837efb731a15
https://doi.org/10.1609/aaai.v36i8.20873
https://doi.org/10.1609/aaai.v36i8.20873
Autor:
Xiaohan Chen, Yang Zhao, Yue Wang, Pengfei Xu, Haoran You, Chaojian Li, Yonggan Fu, Yingyan Lin, Zhangyang Wang
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-15
The record-breaking performance of deep neural networks (DNNs) comes with heavy parameter budgets, which leads to external dynamic random access memory (DRAM) for storage. The prohibitive energy of DRAM accesses makes it nontrivial for DNN deployment
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0790b10c5a0cf2b1d32160e77076fb76
https://doi.org/10.1109/tnnls.2021.3138056
https://doi.org/10.1109/tnnls.2021.3138056
Autor:
Hongyan Chen, Hung‐Hsiang Yang, Timo Frauhammer, Haoran You, Qing Sun, Peter Nagel, Stefan Schuppler, Ana Belén Gaspar, José Antonio Real, Wulf Wulfhekel
Publikováno v:
Small, 19 (22), Art.-Nr.: 2300251
Spin crossover (SCO) complexes sensitively react on changes of the environment by a change in the spin of the central metallic ion making them ideal candidates for molecular spintronics. In particular, the composite of SCO complexes and ferromagnetic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac9ee95834dba3b893810f6c573b1129
https://publikationen.bibliothek.kit.edu/1000156468/150393474
https://publikationen.bibliothek.kit.edu/1000156468/150393474
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 32:4389-4403
Recent techniques built on Generative Adversarial Networks (GANs), such as Cycle-Consistent GANs, are able to learn mappings among different domains built from unpaired datasets, through min-max optimization games between generators and discriminator
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d68a07e314ef5879c92f3ed6aaf6161
https://doi.org/10.1109/tnnls.2020.3017669
https://doi.org/10.1109/tnnls.2020.3017669
Autor:
Yang Zhao, Ziyun Li, Yonggan Fu, Yongan Zhang, Chaojian Li, Cheng Wan, Haoran You, Shang Wu, Xu Ouyang, Vivek Boominathan, Ashok Veeraraghavan, Yingyan Lin
Publikováno v:
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::673e7dd9da0a6cd58e45943595d87fce
https://doi.org/10.1109/vlsitechnologyandcir46769.2022.9830458
https://doi.org/10.1109/vlsitechnologyandcir46769.2022.9830458
Autor:
Haoran You, Cheng Wan, Yang Zhao, Zhongzhi Yu, Yonggan Fu, Jiayi Yuan, Shang Wu, Shunyao Zhang, Yongan Zhang, Chaojian Li, Vivek Boominathan, Ashok Veeraraghavan, Ziyun Li, Yingyan Lin
Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring high throughput (e.g., 240 FPS), small-form, and enhanced visual privac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f41f683b18fb587add4b97ddb694b709
http://arxiv.org/abs/2206.00877
http://arxiv.org/abs/2206.00877
Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications. To tackle this limitation, pioneering wor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfd2deaaca0ca8b767f593cb2094c54a