Search Results for author: Yanchun Liang

Found 8 papers, 5 papers with code

Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

1 code implementation25 Aug 2021 Xuan Wu, Jizong Han, Di Wang, Pengyue Gao, Quanlong Cui, Liang Chen, Yanchun Liang, Han Huang, Heow Pueh Lee, Chunyan Miao, You Zhou, Chunguo Wu

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness.

Single Particle Analysis

Neural Architecture Search based on Cartesian Genetic Programming Coding Method

no code implementations12 Mar 2021 Xuan Wu, Linhan Jia, Xiuyi Zhang, Liang Chen, Yanchun Liang, You Zhou, Chunguo Wu

To evolve the architectures under the framework of CGP, the operations such as convolution are identified as the types of function nodes of CGP, and the evolutionary operations are designed based on Evolutionary Strategy.

BIG-bench Machine Learning Neural Architecture Search +2

Deep Learning Analysis and Age Prediction from Shoeprints

1 code implementation7 Nov 2020 Muhammad Hassan, Yan Wang, Di Wang, Daixi Li, Yanchun Liang, You Zhou, Dong Xu

We collected 100, 000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age.

Gender Classification

Crossed-Time Delay Neural Network for Speaker Recognition

2 code implementations31 May 2020 Liang Chen, Yanchun Liang, Xiaohu Shi, You Zhou, Chunguo Wu

Time Delay Neural Network (TDNN) is a well-performing structure for DNN-based speaker recognition systems.

Speaker Recognition Speaker Verification

Compositional Learning of Relation Path Embedding for Knowledge Base Completion

no code implementations22 Nov 2016 Xixun Lin, Yanchun Liang, Fausto Giunchiglia, Xiaoyue Feng, Renchu Guan

In this paper, we study the problem of how to better embed entities and relations of knowledge bases into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths, and we propose a compositional learning model of relation path embedding (RPE).

Knowledge Base Completion Relation

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