no code implementations • 30 Apr 2024 • John Beverley, Robin McGill, Sam Smith, Jie Zheng, Giacomo De Colle, Finn Wilson, Matthew Diller, William D. Duncan, William R. Hogan, Yongqun He
The term credential encompasses educational certificates, degrees, certifications, and government-issued licenses.
1 code implementation • 6 Sep 2023 • Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, Erik Cambria
Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations.
no code implementations • 30 Jul 2023 • Jianan Xie, Ji Liu, Chi Zhang, Xihui Chen, Ping Huai, Jie Zheng, Xiaofeng Zhang
Th is heavy dependence on labeled datasets will seriously restrict the application of networks, because it is very costly to annotate a large number of diffraction patterns.
1 code implementation • 11 Apr 2023 • Xinnan Dai, Caihua Shan, Jie Zheng, Xiaoxiao Li, Dongsheng Li
BFReg-NN starts from gene expression data and is capable of merging most existing biological knowledge into the model, including the regulatory relations among genes or proteins (e. g., gene regulatory networks (GRN), protein-protein interaction networks (PPI)) and the hierarchical relations among genes, proteins and pathways (e. g., several genes/proteins are contained in a pathway).
no code implementations • 30 Mar 2023 • Hasin Rehana, Nur Bengisu Çam, Mert Basmaci, Jie Zheng, Christianah Jemiyo, Yongqun He, Arzucan Özgür, Junguk Hur
It achieved a precision of 88. 37%, a recall of 85. 14%, and an F1-score of 86. 49% on the LLL dataset.
no code implementations • 26 May 2022 • Qingpeng Cai, Ruohan Zhan, Chi Zhang, Jie Zheng, Guangwei Ding, Pinghua Gong, Dong Zheng, Peng Jiang
In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.
1 code implementation • Findings (ACL) 2022 • Rui Cao, Yihao Wang, Yuxin Liang, Ling Gao, Jie Zheng, Jie Ren, Zheng Wang
We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples.
no code implementations • 20 Oct 2021 • Yihao Wang, Ling Gao, Jie Ren, Rui Cao, Hai Wang, Jie Zheng, Quanli Gao
In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e. g., brightness, saturation).
no code implementations • 20 Aug 2021 • Weicong Ding, Hanlin Tang, Jingshuo Feng, Lei Yuan, Sen yang, Guangxu Yang, Jie Zheng, Jing Wang, Qiang Su, Dong Zheng, Xuezhong Qiu, Yongqi Liu, Yuxuan Chen, Yang Liu, Chao Song, Dongying Kong, Kai Ren, Peng Jiang, Qiao Lian, Ji Liu
In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter.
1 code implementation • 12 Apr 2021 • Yuxin Liang, Rui Cao, Jie Zheng, Jie Ren, Ling Gao
We train the weights on word similarity tasks and show that processed embedding is more isotropic.
no code implementations • 21 Oct 2018 • Qing Qin, Jie Ren, Jialong Yu, Ling Gao, Hai Wang, Jie Zheng, Yansong Feng, Jianbin Fang, Zheng Wang
We experimentally show that how two mainstream compression techniques, data quantization and pruning, perform on these network architectures and the implications of compression techniques to the model storage size, inference time, energy consumption and performance metrics.
no code implementations • 20 Oct 2018 • Yong Liu, Min Wu, Chenghao Liu, Xiao-Li Li, Jie Zheng
Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).
no code implementations • 10 Jul 2018 • Shenghua He, Jie Zheng, Akiko Maehara, Gary Mintz, Dalin Tang, Mark Anastasio, Hua Li
Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images.