59 citations · 83 across the 17 of their papers we have counts for
17 papers
IgPose: A Generative Data-Augmented Pipeline for Robust Immunoglobulin-Antigen Binding Prediction
Tien-Cuong Bui, Injae Chung, Wonjun Lee +2
Predicting immunoglobulin-antigen (Ig-Ag) binding remains a significant challenge due to the paucity of experimentally-resolved complexes and the limited accuracy of de novo Ig str…
Enhancing Explainability of Graph Neural Networks Through Conceptual and Structural Analyses and Their Extensions
Tien Cuong Bui
Graph Neural Networks (GNNs) have become a powerful tool for modeling and analyzing data with graph structures. The wide adoption in numerous applications underscores the value of…
Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG
Jisoo Jang, Tien-Cuong Bui, Yunjun Choi +1
This paper introduces an Error Correction through Prompt Tuning for NL-to-SQL, leveraging the latest advancements in generative pre-training-based LLMs and RAG. Our work addresses…
RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis -- A Case Study for the Semiconductor Sector
Hai-Thien To, Tien-Cuong Bui, Van-Duc Le
Financial analysis relies heavily on the interpretation of earnings reports to assess company performance and guide decision-making. Traditional methods for generating such analyze…
VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data
Van-Duc Le, Tien-Cuong Bui, Wen-Syan Li
An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model…
Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting
Van-Duc Le, Tien-Cuong Bui, Sang-Kyun Cha
Citywide Air Pollution Forecasting tries to precisely predict the air quality multiple hours ahead for the entire city. This topic is challenged since air pollution varies in a spa…
INGREX: An Interactive Explanation Framework for Graph Neural Networks
Tien-Cuong Bui, Van-Duc Le, Wen-Syan Li +1
Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions. However, the complexity of GNNs makes it difficult to expl…
Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation
Tien-Cuong Bui, Van-Duc Le, Wen-syan Li +1
Graph Neural Networks (GNNs) have become increasingly ubiquitous in numerous applications and systems, necessitating explanations of their predictions, especially when making criti…
Machine Learning-based Selection of Graph Partitioning Strategy Using the Characteristics of Graph Data and Algorithm
YoungJoon Park, DongKyu Lee, Tien-Cuong Bui
Analyzing large graph data is an essential part of many modern applications, such as social networks. Due to its large computational complexity, distributed processing is frequentl…
PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes
Tien-Cuong Bui, Wen-syan Li, Sang-Kyun Cha
Graph Neural Networks (GNNs) are widely adopted in advanced AI systems due to their capability of representation learning on graph data. Even though GNN explanation is crucial to i…
Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System
Tung-Lam Duong, Van-Duc Le, Tien-Cuong Bui +1
Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability. As the cornerstone of a sm…
Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars
Tien-Cuong Bui, Van-Duc Le, Hai-Thien To +1
Paraphrase generation is a long-standing problem and serves an essential role in many natural language processing problems. Despite some encouraging results, recent methods either…
STAR: Spatio-Temporal Prediction of Air Quality Using A Multimodal Approach
Tien-Cuong Bui, Joonyoung Kim, Taewoo Kang +5
With the increase of global economic activities and high energy demand, many countries have raised concerns about air pollution. However, air quality prediction is a challenging is…
An Attention-Based Speaker Naming Method for Online Adaptation in Non-Fixed Scenarios
Jungwoo Pyo, Joohyun Lee, Youngjune Park +2
A speaker naming task, which finds and identifies the active speaker in a certain movie or drama scene, is crucial for dealing with high-level video analysis applications such as a…
Spatiotemporal deep learning model for citywide air pollution interpolation and prediction
Van-Duc Le, Tien-Cuong Bui, Sang Kyun Cha
Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air…
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM
Tien-Cuong Bui, Van-Duc Le, Sang-Kyun Cha
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's…
Real-time Air Pollution prediction model based on Spatiotemporal Big data
Van-Duc Le, Tien-Cuong Bui, Sang Kyun Cha
Air pollution is one of the most concerns for urban areas. Many countries have constructed monitoring stations to hourly collect pollution values. Recently, there is a research in…