Search Results for author: Yi Cheng

Found 9 papers, 5 papers with code

Research on Parallel SVM Algorithm Based on Cascade SVM

no code implementations11 Mar 2022 Yi Cheng, Xiaoyan, Liu

At the same time, it proves that the accuracy of the model obtained by BCSVM algorithm is higher than that of CSVM.

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

no code implementations ACL 2021 Yi Cheng, SiYao Li, Bang Liu, Ruihui Zhao, Sujian Li, Chenghua Lin, Yefeng Zheng

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels.

Question Answering Question Generation

Unifying Discourse Resources with Dependency Framework

1 code implementation CCL 2021 Yi Cheng, Sujian Li, Yueyuan Li

For text-level discourse analysis, there are various discourse schemes but relatively few labeled data, because discourse research is still immature and it is labor-intensive to annotate the inner logic of a text.

6D Pose Estimation with Correlation Fusion

no code implementations24 Sep 2019 Yi Cheng, Hongyuan Zhu, Ying Sun, Cihan Acar, Wei Jing, Yan Wu, Liyuan Li, Cheston Tan, Joo-Hwee Lim

To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation.

6D Pose Estimation 6D Pose Estimation using RGB

Optimal Adaptive and Accelerated Stochastic Gradient Descent

1 code implementation1 Oct 2018 Qi Deng, Yi Cheng, Guanghui Lan

More specifically, we show that diagonal scaling, initially designed to improve vanilla stochastic gradient, can be incorporated into accelerated stochastic gradient descent to achieve the optimal rate of convergence for smooth stochastic optimization.

Stochastic Optimization

A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion

1 code implementation3 Apr 2017 Lin Xiong, Jayashree Karlekar, Jian Zhao, Yi Cheng, Yan Xu, Jiashi Feng, Sugiri Pranata, ShengMei Shen

In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge.

Face Recognition Transfer Learning

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