6 citations · 18 across the 6 of their papers we have counts for
6 papers
CodeExp: Explanatory Code Document Generation
Haotian Cui, Chenglong Wang, Junjie Huang +5
Developing models that can automatically generate detailed code explanation can greatly benefit software maintenance and programming education. However, existing code-to-text gener…
Execution-based Evaluation for Data Science Code Generation Models
Junjie Huang, Chenglong Wang, Jipeng Zhang +6
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress…
Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks
Zhiyong Dou, Haotian Cui, Lin Zhang +1
Diffusion has shown great success in improving accuracy of unsupervised image retrieval systems by utilizing high-order structures of image manifold. However, existing diffusion me…
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…
Object-oriented Neural Programming (OONP) for Document Understanding
Zhengdong Lu, Xianggen Liu, Haotian Cui +2
We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a pred…