no code implementations • 4 Apr 2024 • Yang Ba, Michelle V. Mancenido, Erin K. Chiou, Rong pan
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in subsequent machine learning tasks.
no code implementations • 5 Dec 2023 • Hao Zhao, Rong pan
We investigate the effectiveness of DACD method in diverse scenarios and show it outperforms other active learning change-point detection approaches.
no code implementations • 22 Jan 2022 • Jingwen Zhang, Yuezhou Wu, Rong pan
To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power.
no code implementations • 7 Jan 2022 • Jingwen Zhang, Yuezhou Wu, Rong pan
Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML.
no code implementations • 3 Jul 2021 • Xiao Liu, Rong pan
Boost-R constructs an ensemble of gradient boosted additive trees to estimate the cumulative intensity function of the recurrent event process, where a new tree is added to the ensemble by minimizing the regularized L2 distance between the observed and predicted cumulative intensity.
1 code implementation • 30 Apr 2021 • Huijuan Wang, Shuangyin Li, Rong pan
Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods.
no code implementations • ICCV 2021 • Ge Gao, Pei You, Rong pan, Shunyuan Han, Yuanyuan Zhang, Yuchao Dai, Hojae Lee
In recent years, neural image compression emerges as a rapidly developing topic in computer vision, where the state-of-the-art approaches now exhibit superior compression performance than their conventional counterparts.
no code implementations • IJCNLP 2019 • Heng Wang, Shuangyin Li, Rong pan, Mingzhi Mao
Meanwhile, a novel mechanism of reinforcement learning is proposed by forcing an agent to walk forward every step to avoid the agent stalling at the same entity node constantly.
no code implementations • WS 2019 • Feng Nie, Jinpeng Wang, Rong pan, Chin-Yew Lin
Data-to-text generation aims to generate descriptions given a structured input data (i. e., a table with multiple records).
no code implementations • ACL 2019 • Feng Nie, Jin-Ge Yao, Jinpeng Wang, Rong pan, Chin-Yew Lin
Recent neural language generation systems often \textit{hallucinate} contents (i. e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text.
1 code implementation • 4 Nov 2018 • Peifeng Wang, Jialong Han, Chenliang Li, Rong pan
Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors.
no code implementations • CONLL 2018 • Feng Nie, Shuyan Zhou, Jing Liu, Jinpeng Wang, Chin-Yew Lin, Rong pan
The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base.
no code implementations • EMNLP 2018 • Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong pan, Chin-Yew Lin
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
no code implementations • 23 Sep 2018 • Peifeng Wang, Shuangyin Li, Rong pan
In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples.
1 code implementation • 8 Sep 2018 • Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong pan, Chin-Yew Lin
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
no code implementations • 15 Aug 2018 • Feng Nie, Hailin Chen, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin, Rong pan
Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts.
no code implementations • 7 Nov 2016 • Huabin Zheng, Jingyu Wang, Zhengjie Huang, Yang Yang, Rong pan
We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation.
no code implementations • 30 Jul 2015 • Shuangyin Li, Jiefei Li, Guan Huang, Ruiyang Tan, Rong Pan
We propose a novel method to model the SSDs by a so-called Tag-Weighted Topic Model (TWTM).