Search Results for author: Shuai Lu

Found 8 papers, 3 papers with code

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

no code implementations17 Sep 2021 Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy

While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window.

Code Completion Code Generation +3

WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach

1 code implementation5 Apr 2021 JunJie Huang, Duyu Tang, Wanjun Zhong, Shuai Lu, Linjun Shou, Ming Gong, Daxin Jiang, Nan Duan

In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings.

Sentence Embedding

CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

no code implementations22 Sep 2020 Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, Shuai Ma

Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models.

Code Translation Translation

GraphCodeBERT: Pre-training Code Representations with Data Flow

2 code implementations ICLR 2021 Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou

Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.

Clone Detection Code Completion +5

Analysis of regularized Nyström subsampling for regression functions of low smoothness

no code implementations3 Jun 2018 Shuai Lu, Peter Mathé, Sergiy Pereverzyev Jr

This paper studies a Nystr\"om type subsampling approach to large kernel learning methods in the misspecified case, where the target function is not assumed to belong to the reproducing kernel Hilbert space generated by the underlying kernel.

Cannot find the paper you are looking for? You can Submit a new open access paper.