no code implementations • CCL 2022 • Wenxin Li, Hongying Zan, Tongfeng Guan, Yingjie Han
“期货领域是数据最丰富的领域之一, 本文以商品期货的研究报告为数据来源构建了期货领域知识图谱(Commodity Futures Knowledge Graph, CFKG)。以期货产品为核心, 确立了概念分类体系及关系描述体系, 形成图谱的概念层;在MHS-BIA与GPN模型的基础上, 通过领域专家指导对242万字的研报文本进行标注与校对, 形成了CFKG数据层, 并设计了可视化查询系统。所构建的CFKG包含17, 003个农产品期货关系三元组、13, 703种非农产品期货关系三元组, 为期货领域文本分析、舆情监控和推理决策等应用提供知识支持。”
no code implementations • 3 Sep 2023 • Rui Zhu, Quanzhou Hu, Wenxin Li, Honghao Xiao, Chaogang Wang, Zixin Zhou
Business process document generation is a crucial stage in BPM.
no code implementations • 22 Jun 2022 • Xinyu Zhang, Peng Peng, Yushan Zhou, Haifeng Wang, Wenxin Li
First, there is inaccuracy when analysing the simplified payoff table.
no code implementations • 16 Dec 2020 • Wenxin Li
We show that for the problem of minimizing (or maximizing) the ratio of two supermodular functions, no bounded approximation ratio can be achieved via polynomial number of queries, if the two supermodular functions are both monotone non-decreasing or non-increasing.
no code implementations • 16 Jun 2020 • Wenxin Li, Moran Feldman, Ehsan Kazemi, Amin Karbasi
In this paper, we provide the first deterministic algorithm that achieves the tight $1-1/e$ approximation guarantee for submodular maximization under a cardinality (size) constraint while making a number of queries that scales only linearly with the size of the ground set $n$.
1 code implementation • 27 Mar 2020 • Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxin Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
1 code implementation • 7 Feb 2019 • Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
no code implementations • 14 Nov 2018 • Haifeng Zhang, Zilong Guo, Han Cai, Chris Wang, Wei-Nan Zhang, Yong Yu, Wenxin Li, Jun Wang
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.
no code implementations • 8 May 2018 • Yi Zhang, Zhengfei Wang, Guoxiong Xu, Hongshi Huang, Wenxin Li
Plantar pressure is one of which contains this information and it describes human walking features.
no code implementations • 28 Feb 2018 • Guoxiong Xu, Zhengfei Wang, Hongshi Huang, Wenxin Li, Can Liu, Shilei Liu
Here, we propose a model using convolutional neural network based on plantar pressure for medical diagnosis.
no code implementations • 4 Jan 2018 • Yi Zhang, Houjun Huang, Haifeng Zhang, Liao Ni, Wei Xu, Nasir Uddin Ahmed, Md. Shakil Ahmed, Yilun Jin, Yingjie Chen, Jingxuan Wen, Wenxin Li
The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets.
no code implementations • 5 Jul 2017 • Haifeng Zhang, Jun Wang, Zhiming Zhou, Wei-Nan Zhang, Ying Wen, Yong Yu, Wenxin Li
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.
no code implementations • 17 Dec 2016 • Liao Ni, Yi Zhang, He Zheng, Shilei Liu, Houjun Huang, Wenxin Li
Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method incorporating Weighted Voting.