Search Results for author:
Found 14 papers, 6 papers with code
BadEdit: Backdooring large language models by model editing
It boasts superiority over existing backdoor injection techniques in several areas: (1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples).
Multi-target Backdoor Attacks for Code Pre-trained Models
We evaluate our approach on two code understanding tasks and three code generation tasks over seven datasets.
LMs: Understanding Code Syntax and Semantics for Code Analysis
We break down the abilities needed for artificial intelligence~(AI) models to address SE tasks related to code analysis into three categories: 1) syntax understanding, 2) static behavior understanding, and 3) dynamic behavior understanding.
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search
Furthermore, the perturbation of adversarial examples introduced by RNNS is smaller compared to the baselines in terms of the number of replaced variables and the change in variable length.
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
These structures are fundamental to understanding code.
Learning Program Representations with a Tree-Structured Transformer
Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks.
CommitBART: A Large Pre-trained Model for GitHub Commits
GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution.
Learning Program Semantics with Code Representations: An Empirical Study
However, currently, a comprehensive and systematic study on evaluating different program representation techniques across diverse tasks is still missed.
GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
Specifically, we propose to construct graphs for the source code and queries with bidirectional GGNN (BiGGNN) to capture the local structural information of the source code and queries.
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN
However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries.
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models.
Software Engineering
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Vulnerability identification is crucial to protect the software systems from attacks for cyber security.
DeepCount: Crowd Counting with WiFi via Deep Learning
By massive training samples, our end-to-end learning approach can achieve an average of 86. 4% prediction accuracy in an environment of up to 5 people.
