Search Results for author: Yanfei Li

Found 7 papers, 2 papers with code

Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids

no code implementations15 Mar 2024 Yanfei Li, Juejing Liu, Xiaodong Zhao, Wenjun Liu, Tong Geng, Ang Li, Xin Zhang

Traditional analysis of highly distorted micro-X-ray diffraction ({\mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data.

Binary Classification Multi-Task Learning

Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver

no code implementations14 Aug 2023 Kaili Jiang, Dechang Wang, Kailun Tian, HanCong Feng, Yuxin Zhao, Sen Cao, Jian Gao, Xuying Zhang, Yanfei Li, Junyu Yuan, Ying Xiong, Bin Tang

To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper.

Searching Similarity Measure for Binarized Neural Networks

no code implementations5 Jun 2022 Yanfei Li, Ang Li, Huimin Yu

Being a promising model to be deployed in resource-limited devices, Binarized Neural Networks (BNNs) have drawn extensive attention from both academic and industry.

GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm

1 code implementation5 Jun 2022 Yanfei Li, Tong Geng, Samuel Stein, Ang Li, Huimin Yu

To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs.

Binarization

BCNN: Binary Complex Neural Network

no code implementations28 Mar 2021 Yanfei Li, Tong Geng, Ang Li, Huimin Yu

Motivated by the complex neural networks, in this paper we introduce complex representation into the BNNs and propose Binary complex neural network -- a novel network design that processes binary complex inputs and weights through complex convolution, but still can harvest the extraordinary computation efficiency of BNNs.

Cannot find the paper you are looking for? You can Submit a new open access paper.