9 citations · 20 across the 6 of their papers we have counts for
6 papers
Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model
Yizhou Zhang, Loc Trinh, Defu Cao +2
Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions. Unlike traditional rumors or fake news editors who mainly rely on g…
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Defu Cao, Yousef El-Laham, Loc Trinh +2
In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a g…
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids
Xiangtian Zheng, Nan Xu, Loc Trinh +5
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable…
An Examination of Fairness of AI Models for Deepfake Detection
Loc Trinh, Yan Liu
Recent studies have demonstrated that deep learning models can discriminate based on protected classes like race and gender. In this work, we evaluate bias present in deepfake data…
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
Chuizheng Meng, Loc Trinh, Nan Xu +1
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature…
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
Loc Trinh, Michael Tsang, Sirisha Rambhatla +1
In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations. Currently, most state-of-t…