5 citations · 15 across the 17 of their papers we have counts for
19 papers
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
Duong M. Nguyen, Trong Nghia Hoang, Hang Thi Nguyen +3
Histological whole-slide images (WSIs) are central to computational pathology but pose severe computational challenges due to their extremely high resolution, often spanning severa…
Cost-Aware Uplink MPQUIC Scheduling via Multi-Objective Bayesian Optimization
Thanh Trung Nguyen, Thanh Le, Phi Le Nguyen +1
Multipath QUIC (MPQUIC) enables simultaneous uplink transmission over heterogeneous access networks such as Wi-Fi and LTE, improving reliability and performance. However, aggressiv…
Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space
Long Minh Bui, Tuan Anh Le Van, Tung Phi Duc +3
Model merging aims to combine existing single-task solutions into a multi-task solution without additional data-driven fine-tuning.~Most existing approaches achieve this using geom…
Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm
Trong Khiem Tran, Anh Duc Chu, Quang Hung Pham +2
Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student model building on another typ…
Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
Minh Khoi Nguyen, Dai Lam Le, Amir Reza Jafari +8
Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet incorrect statements, raising si…
Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders
Tue M. Cao, Hoang X. Nhat, Raed Alharbi +2
Learning hierarchical features in Sparse Autoencoders (SAEs) is essential for capturing the structured nature of real-world data and mitigating issues like feature absorption or sp…
Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
Nang Hung Nguyen, Phi Le Nguyen, Thao Nguyen Truong +2
This paper introduces a new framework for recovering causal graphs from observational data, leveraging the observation that the distribution of an effect, conditioned on its causes…
Diffusion-Inspired Reconfiguration of Transformers for Uncertainty Calibration
Manh Cuong Dao, Quang Hung Pham, Phi Le Nguyen +3
Uncertainty calibration in pre-trained transformers is critical for their reliable deployment in risk-sensitive applications. Yet, most existing pre-trained transformers do not hav…
ConstStyle: Robust Domain Generalization with Unified Style Transformation
Nam Duong Tran, Nam Nguyen Phuong, Hieu H. Pham +2
Deep neural networks often suffer performance drops when test data distribution differs from training data. Domain Generalization (DG) aims to address this by focusing on domain-in…
Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models
Manh Duong Nguyen, Dac Thai Nguyen, Trung Viet Nguyen +3
Osteosarcoma, the most common primary bone cancer, often requires accurate necrosis assessment from whole slide images (WSIs) for effective treatment planning and prognosis. Howeve…
Heterogeneous Hypergraph Embedding for Recommendation Systems
Darnbi Sakong, Viet Hung Vu, Thanh Trung Huynh +4
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is…
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen +4
Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data. The increasing demand to address i…
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren +4
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and exp…
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
Thuy Dung Nguyen, Anh Duy Nguyen, Kok-Seng Wong +4
Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, e…
High Accurate and Explainable Multi-Pill Detection Framework with Graph Neural Network-Assisted Multimodal Data Fusion
Anh Duy Nguyen, Huy Hieu Pham, Huynh Thanh Trung +3
Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identific…
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization
Nang Hung Nguyen, Duc Long Nguyen, Trong Bang Nguyen +4
Federated learning enables edge devices to train a global model collaboratively without exposing their data. Despite achieving outstanding advantages in computing efficiency and pr…
Image-based Contextual Pill Recognition with Medical Knowledge Graph Assistance
Anh Duy Nguyen, Thuy Dung Nguyen, Huy Hieu Pham +2
Identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the…
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss
Bao Hieu Tran, Thanh Le-Cong, Huu Manh Nguyen +3
In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications.…
Usability and Aesthetics: Better Together for Automated Repair of Web Pages
Thanh Le-Cong, Xuan Bach D. Le, Quyet-Thang Huynh +1
With the recent explosive growth of mobile devices such as smartphones or tablets, guaranteeing consistent web appearance across all environments has become a significant problem.…