no code implementations • 9 Nov 2022 • Yuanlong Li, Gaopan Huang, Min Zhou, Chuan Fu, Honglin Qiao, Yan He
Learning an explainable classifier often results in low accuracy model or ends up with a huge rule set, while learning a deep model is usually more capable of handling noisy data at scale, but with the cost of hard to explain the result and weak at generalization.
no code implementations • 31 Dec 2021 • Zhanghao Yu, Joshua C. Chen, Yan He, Fatima T. Alrashdan, Benjamin W. Avants, Amanda Singer, Jacob T. Robinson, Kaiyuan Yang
This article presents a hardware platform including stimulating implants wirelessly powered and controlled by a shared transmitter (TX) for coordinated leadless multisite stimulation.
no code implementations • 7 Jul 2021 • Zhanghao Yu, Joshua C. Chen, Fatima T. Alrashdan, Benjamin W. Avants, Yan He, Amanda Singer, Jacob T. Robinson, Kaiyuan Yang
This paper presents the first wireless and programmable neural stimulator leveraging magnetoelectric (ME) effects for power and data transfer.
no code implementations • 6 Jul 2021 • Zhiyu Chen, Zhanghao Yu, Qing Jin, Yan He, Jingyu Wang, Sheng Lin, Dai Li, Yanzhi Wang, Kaiyuan Yang
A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference.
no code implementations • 11 Feb 2021 • Zhen-Bing Dai, Zhiqiang Li, Yan He
We study the optical response of bilayer graphene with a kink potential composed of a domain wall separating two AB regions with opposite interlayer bias.
Mesoscale and Nanoscale Physics
no code implementations • 29 Nov 2020 • Yan He, Jifang Qiu, Chang Liu, Yue Liu, Jian Wu
The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC).
no code implementations • 19 Aug 2020 • Sheng Ren, Yan He, Neal N. Xiong, Kehua Guo
For example, if the input images are of the same type, the current incremental model can learn new knowledge while not forgetting old knowledge.
1 code implementation • 28 Aug 2019 • Siwang Zhou, Yan He, Yonghe Liu, Chengqing Li, Jianming Zhang
Specifically, with our multichannel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model.