Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Yiren Zhao"'
Publikováno v:
Kouqiang yixue, Vol 44, Iss 6, Pp 426-432 (2024)
Objective To explore the effect of FBXW7 on ferroptosis in head and neck squamous cell carcinoma. Methods Head and neck squamous cell lines HN4 and HN6 were cultured in vitro. FBXW7 and SOX2 overexpression plasmids were constructed, and the plasmids
Externí odkaz:
https://doaj.org/article/50f74f58eb0b4af2a9a38b678d288bd5
Publikováno v:
IEEE Access, Vol 12, Pp 54272-54284 (2024)
With the rapid advancement of quantitative trading technology, the demand for low-latency in Level 2 Deep Market Quote (L2DMQ) Decoding is ever-increasing. The L2DMQ decoder faces increasingly significant challenges in terms of bandwidth and performa
Externí odkaz:
https://doaj.org/article/5d0ed10f90eb4092b812ad747e65d39b
Publikováno v:
EuroS&P
The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While this enabled us to train large-scale neural networks in datacenters and deploy them on edge devices, the focus so far
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1dcce18b29cad95c5a413af32511ce7b
https://doi.org/10.1109/eurosp51992.2021.00024
https://doi.org/10.1109/eurosp51992.2021.00024
Publikováno v:
DSN Workshops
Recent research on reinforcement learning (RL) has suggested that trained agents are vulnerable to maliciously crafted adversarial samples. In this work, we show how such samples can be generalised from White-box and Grey-box attacks to a strong Blac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::093b3861aa8933cfc179c6f4d779f240
Publikováno v:
AISec@CCS
Convolutional Neural Networks (CNNs) are deployed in more and more classification systems, but adversarial samples can be maliciously crafted to trick them, and are becoming a real threat. There have been various proposals to improve CNNs' adversaria
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::144ebae1d967108903af6f8d699ccf8d
https://doi.org/10.1145/3411508.3421381
https://doi.org/10.1145/3411508.3421381
Autor:
Robert Mullins, Cheng-Zhong Xu, Junyi Liu, Yiren Zhao, Xuan Guo, George A. Constantinides, Erwei Wang, Peter Y. K. Cheung, Xitong Gao
Publikováno v:
FPT
Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet real applications often require high throughput and low latency. To help tackle these problems, we propose Tomato, a framework designed to automate the process of gen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00daf6158acfcbc0c03c74d6b13c6a24
Publikováno v:
ISPASS
Hardware accelerators for inference with neural networks can take advantage of the properties of data they process. Performance gains and reduced memory bandwidth during inference have been demonstrated by using narrower data types [1] [2] and by exp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86fe36cfe307ac0eba132ce69b7b658c
Publikováno v:
Web of Science
Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision. However, they can be tricked into misclassifying specially crafted `adversarial' samples -- and samples built to trick one model often work alarmin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::818c8f5364ebe7ad12958e0c7044a455
Publikováno v:
EMDL@MobiSys
Deep Neural Networks (DNNs) have proved to be a conve- nient and powerful tool for a wide range of problems. How- ever, the extensive computational and memory resource re- quirements hinder the adoption of DNNs in resource-con- strained scenarios. Ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb8d41c137d1f4c2de805a3115ec341a
https://doi.org/10.1145/3212725.3212726
https://doi.org/10.1145/3212725.3212726
Autor:
Peter Y. K. Cheung, Julian Faraone, David B. Thomas, Yiren Zhao, Philip H. W. Leong, Junyi Liu, Jiang Su
Publikováno v:
Applied Reconfigurable Computing. Architectures, Tools, and Applications ISBN: 9783319788890
ARC
ARC
Modern Convolutional Neural Networks (CNNs) excel in image classification and recognition applications on large-scale datasets such as ImageNet, compared to many conventional feature-based computer vision algorithms. However, the high computational c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0dd4487e88f570633ddd51cde1d2bfe6
https://doi.org/10.1007/978-3-319-78890-6_2
https://doi.org/10.1007/978-3-319-78890-6_2