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Found 12 papers, 5 papers with code
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
To this end, we survey the related literature and identify 10 commonly adopted empirical evaluation hazards that may significantly impact experimental results.
Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data.
CodeLens: An Interactive Tool for Visualizing Code Representations
Representing source code in a generic input format is crucial to automate software engineering tasks, e. g., applying machine learning algorithms to extract information.
Boosting Source Code Learning with Data Augmentation: An Empirical Study
The next era of program understanding is being propelled by the use of machine learning to solve software problems.
MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation
Data augmentation has been a popular approach to supplement training data in domains such as computer vision and NLP.
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification.
Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
Recent studies show that test selection for DNN is a promising direction that tackles this issue by selecting minimal representative data to label and using these data to assess the model.
CodeS: Towards Code Model Generalization Under Distribution Shift
Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation.
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
Applying deep learning to science is a new trend in recent years which leads DL engineering to become an important problem.
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
The results reveal that 1) data with distribution shifts happen more disagreements than without.
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models
Our acquisition function -- named density-based robust sampling with entropy (DRE) -- outperforms the other acquisition functions (including random) in terms of robustness by up to 24. 40\% (3. 84\% than random particularly), while remaining competitive on accuracy.
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles
In this paper, we propose MUTEN, a low-cost method to improve the success rate of well-known attacks against gradient-masking models.
