3 citations · 11 across the 38 of their papers we have counts for
12 papers · 1 filter
Unveiling Concept Attribution in Diffusion Models
Quang H. Nguyen, Hoang Phan, Khoa D. Doan
Diffusion models have shown remarkable abilities in generating realistic and high-quality images from text prompts. However, a trained model remains largely black-box; little do we…
Class-Aware Contrastive Optimization for Imbalanced Text Classification
Grigorii Khvatskii, Nuno Moniz, Khoa Doan +1
The unique characteristics of text data make classification tasks a complex problem. Advances in unsupervised and semi-supervised learning and autoencoder architectures addressed s…
Flatness-aware Sequential Learning Generates Resilient Backdoors
Hoang Pham, The-Anh Ta, Anh Tran +1
Recently, backdoor attacks have become an emerging threat to the security of machine learning models. From the adversary's perspective, the implanted backdoors should be resistant…
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…
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator
Thinh Nguyen, Khoa D Doan, Binh T. Nguyen +2
Federated Class-Incremental Learning (FCIL) increasingly becomes important in the decentralized setting, where it enables multiple participants to collaboratively train a global mo…
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning
Son Nguyen, Thinh Nguyen, Khoa D Doan +1
Federated Learning (FL) is a distributed machine learning approach that maintains data privacy by training on decentralized data sources. Similar to centralized machine learning, F…
Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape
Tuan Nguyen, Dung Thuy Nguyen, Khoa D Doan +1
Despite the promise of Federated Learning (FL) for privacy-preserving model training on distributed data, it remains susceptible to backdoor attacks. These attacks manipulate model…
Clean-Label Physical Backdoor Attacks with Data Distillation
Thinh Dao, Khoa D Doan, Kok-Seng Wong
Deep Neural Networks (DNNs) are shown to be vulnerable to backdoor poisoning attacks, with most research focusing on digital triggers -- artificial patterns added to test-time inpu…
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
Quang H. Nguyen, Thinh Dao, Duy C. Hoang +4
The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and…
Composite Concept Extraction through Backdooring
Banibrata Ghosh, Haripriya Harikumar, Khoa D Doan +2
Learning composite concepts, such as \textquotedbl red car\textquotedbl , from individual examples -- like a white car representing the concept of \textquotedbl car\textquotedbl{}…
Cold-start Recommendation by Personalized Embedding Region Elicitation
Hieu Trung Nguyen, Duy Nguyen, Khoa Doan +1
Rating elicitation is a success element for recommender systems to perform well at cold-starting, in which the systems need to recommend items to a newly arrived user with no prior…
Retrospective Feature Estimation for Continual Learning
Nghia D. Nguyen, Hieu Trung Nguyen, Ang Li +3
The intrinsic capability to continuously learn a changing data stream is a desideratum of deep neural networks (DNNs). However, current DNNs suffer from catastrophic forgetting, wh…