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Found 9 papers, 7 papers with code
Investigating White-Box Attacks for On-Device Models
Our findings emphasize the need for developers to carefully consider their model deployment strategies, and use white-box methods to evaluate the vulnerability of on-device models.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies.
Modularizing while Training: A New Paradigm for Modularizing DNN Models
In this paper, we propose a novel approach that incorporates modularization into the model training process, i. e., modularizing-while-training (MwT).
Reusing Deep Neural Network Models through Model Re-engineering
Prior approaches to DNN model reuse have two main limitations: 1) reusing the entire model, while only a small part of the model's functionalities (labels) are required, would cause much overhead (e. g., computational and time costs for inference), and 2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack.
Learning from Noisy Crowd Labels with Logics
This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels.
Patching Weak Convolutional Neural Network Models through Modularization and Composition
To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model.
Learning from Multiple Annotators by Incorporating Instance Features
Learning from multiple annotators aims to induce a high-quality classifier from training instances, where each of them is associated with a set of possibly noisy labels provided by multiple annotators under the influence of their varying abilities and own biases.
A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques
Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments.
Software Engineering
