23 citations · 29 across the 7 of their papers we have counts for
7 papers
Improving LLM Unlearning Robustness via Random Perturbations
Dang Huu-Tien, Hoang Thanh-Tung, Anh Bui +3
Here, we show that current LLM unlearning methods inherently reduce models' robustness, causing them to misbehave even when a single non-adversarial forget-token is present in the…
Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks
Quang H. Nguyen, Nguyen Ngoc-Hieu, The-Anh Ta +4
Deep neural networks are vulnerable to backdoor attacks, a type of adversarial attack that poisons the training data to manipulate the behavior of models trained on such data. Clea…
A Cosine Similarity-based Method for Out-of-Distribution Detection
Nguyen Ngoc-Hieu, Nguyen Hung-Quang, The-Anh Ta +3
The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical…
Class based Influence Functions for Error Detection
Thang Nguyen-Duc, Hoang Thanh-Tung, Quan Hung Tran +4
Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we…
Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora
Anh T. V. Dau, Thang Nguyen-Duc, Hoang Thanh-Tung +1
Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is…
Toward a Generalization Metric for Deep Generative Models
Hoang Thanh-Tung, Truyen Tran
Measuring the generalization capacity of Deep Generative Models (DGMs) is difficult because of the curse of dimensionality. Evaluation metrics for DGMs such as Inception Score, Fré…
On Catastrophic Forgetting and Mode Collapse in Generative Adversarial Networks
Hoang Thanh-Tung, Truyen Tran
In this paper, we show that Generative Adversarial Networks (GANs) suffer from catastrophic forgetting even when they are trained to approximate a single target distribution. We sh…