no code implementations • 15 Aug 2023 • Wen Zan, Yaopeng Han, Xiaotian Jiang, Yao Xiao, Yang Yang, Dayao Chen, Sheng Chen
At pretraining stage, we propose an effective pretraining method that employs both query and multiple fields of document as inputs, including an effective information compression method for lengthy fields.
no code implementations • 1 Sep 2021 • Xiaotian Jiang, Danshi Wang, Qirui Fan, Min Zhang, Chao Lu, Alan Pak Tao Lau
A physics-informed neural network (PINN) that combines deep learning with physics is studied to solve the nonlinear Schr\"odinger equation for learning nonlinear dynamics in fiber optics.
no code implementations • NAACL 2019 • Xiaotian Jiang, Quan Wang, Bin Wang
We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein.
Ranked #10 on Link Prediction on WN18
no code implementations • COLING 2016 • Xiaotian Jiang, Quan Wang, Peng Li, Bin Wang
In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE.