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Found 6 papers, 3 papers with code
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
This efficiency, coupled with its state-of-the-art performance, highlights LaFiCMIL's potential as a groundbreaking approach in the field of large file classification.
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Central to applying ML to software artifacts (like source or executable code) is converting them into forms suitable for learning.
CKG: Dynamic Representation Based on Context and Knowledge Graph
Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance.
Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification
Recently, language representation techniques have achieved great performances in text classification.
AI-driven Mobile Apps: an Explorative Study
In this paper, we conduct the most extensive empirical study on 56, 682 published AI apps from three perspectives: dataset characteristics, development issues, and user feedback and privacy.
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Representation learning of source code is essential for applying machine learning to software engineering tasks.
