Search Results for author:
Found 8 papers, 4 papers with code
Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT
The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news.
Testing the Ability of Language Models to Interpret Figurative Language
Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition.
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
This is the first study of adversarial attacks on GVFL.
Salient Feature Extractor for Adversarial Defense on Deep Neural Networks
Motivated by the observation that adversarial examples are due to the non-robust feature learned from the original dataset by models, we propose the concepts of salient feature(SF) and trivial feature(TF).
GraphAttacker: A General Multi-Task GraphAttack Framework
To address this problem, we propose GraphAttacker, a novel generic graph attack framework that can flexibly adjust the structures and the attack strategies according to the graph analysis tasks.
SUPER: A Novel Lane Detection System
AI-based lane detection algorithms were actively studied over the last few years.
Multi-View Fuzzy Clustering with The Alternative Learning between Shared Hidden Space and Partition
How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge.
Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS.
