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Found 7 papers, 0 papers with code
Predicting Question Quality on StackOverflow with Neural Networks
The wealth of information available through the Internet and social media is unprecedented.
Mutation-Based Adversarial Attacks on Neural Text Detectors
Neural text detectors aim to decide the characteristics that distinguish neural (machine-generated) from human texts.
A Mutation-based Text Generation for Adversarial Machine Learning Applications
Many natural language related applications involve text generation, created by humans or machines.
Synthetic Text Detection: Systemic Literature Review
Within the text analysis and processing fields, generated text attacks have been made easier to create than ever before.
Benchmark Assessment for DeepSpeed Optimization Library
Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics.
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing.
Adversarial Machine Learning in Text Analysis and Generation
The paper summarizes main research trends in the field such as GAN algorithms, models, types of attacks, and defense against those attacks.
