4 papers
Unsupervised Paraphrasing by Simulated Annealing
Xianggen Liu, Lili Mou, Fandong Meng +3
Unsupervised paraphrase generation is a promising and important research topic in natural language processing. We propose UPSA, a novel approach that accomplishes Unsupervised Para…
Shift-based Primitives for Efficient Convolutional Neural Networks
Huasong Zhong, Xianggen Liu, Yihui He +1
We propose a collection of three shift-based primitives for building efficient compact CNN-based networks. These three primitives (channel shift, address shift, shortcut shift) can…
JUMPER: Learning When to Make Classification Decisions in Reading
Xianggen Liu, Lili Mou, Haotian Cui +2
In early years, text classification is typically accomplished by feature-based machine learning models; recently, deep neural networks, as a powerful learning machine, make it poss…
Deep-learning Based Modeling of Fault Detachment Stability for Power Grid
Haotian Cui, Xianggen Liu, Yanhao Huang
The project intends to model the stability of power system with a deep learning algorithm to the problem, aiming to delay the removal of the fault. The so-called "fail-delay cut-of…