no code implementations • EMNLP (Louhi) 2020 • Minghao Zhu, Youzhe Song, Ge Jin, Keyuan Jiang
Post-market surveillance, the practice of monitoring the safe use of pharmaceutical drugs is an important part of pharmacovigilance.
1 code implementation • Expert Systems with Applications 2024 • Qian Zhang, Yi Zhu, Ming Yang, Ge Jin, YingWen Zhu, Qiu Chen
Although sample selection is a mainstream method in the field of learning with noisy labels, which aims to mitigate the impact of noisy labels during model training, the testing performance of these methods exhibits significant fluctuations across different noise rates and types.
Ranked #2 on Learning with noisy labels on Clothing1M
no code implementations • 16 May 2023 • Yunyi Zhou, Zhixuan Chu, Yijia Ruan, Ge Jin, Yuchen Huang, Sheng Li
However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on.
no code implementations • NeurIPS 2021 • Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, Jun Zhou, Jianyong Zhou
Time series forecasting is widely used in business intelligence, e. g., forecast stock market price, sales, and help the analysis of data trend.
no code implementations • ICLR 2019 • Haihao Shen, Jiong Gong, Xiaoli Liu, Guoming Zhang, Ge Jin, and Eric Lin
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.
1 code implementation • 24 Jan 2019 • Qian Liu, Bei Chen, Jian-Guang Lou, Ge Jin, Dongmei Zhang
NLIDB allow users to search databases using natural language instead of SQL-like query languages.
1 code implementation • 4 May 2018 • Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari, Evarist Fomenko, Eden Segal
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.