5 citations · 13 across the 14 of their papers we have counts for
15 papers
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
An Vuong, Minh-Hao Van, Chen Zhao +1
AI for materials science is a critical topic within AI for science, aiming to accelerate materials discovery and produce accurate property predictions. Bilayer 2D material stacking…
Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
An Vuong, Minh-Hao Van, Prateek Verma +2
Vision-Language Models (VLMs) have shown strong performance in tasks like visual question answering and multimodal text generation, but their effectiveness in scientific domains su…
Cross-Modal Attention Guided Unlearning in Vision-Language Models
Karuna Bhaila, Aneesh Komanduri, Minh-Hao Van +1
Vision-Language Models (VLMs) have demonstrated immense capabilities in multi-modal understanding and inference tasks such as Visual Question Answering (VQA), which requires models…
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
Minh-Hao Van, Prateek Verma, Chen Zhao +1
Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discover…
Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models
Minh-Hao Van, Xintao Wu
The rapid evolution of social media has provided enhanced communication channels for individuals to create online content, enabling them to express their thoughts and opinions. Mul…
Fair In-Context Learning via Latent Concept Variables
Karuna Bhaila, Minh-Hao Van, Kennedy Edemacu +3
The emerging in-context learning (ICL) ability of large language models (LLMs) has prompted their use for predictive tasks in various domains with different data types, including t…
Soft Prompting for Unlearning in Large Language Models
Karuna Bhaila, Minh-Hao Van, Xintao Wu
The widespread popularity of Large Language Models (LLMs), partly due to their unique ability to perform in-context learning, has also brought to light the importance of ethical an…
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
Prateek Verma, Minh-Hao Van, Xintao Wu
Vision language models (VLMs) have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data. VLMs such as LLaVA, ChatGP…
Robust Influence-based Training Methods for Noisy Brain MRI
Minh-Hao Van, Alycia N. Carey, Xintao Wu
Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient. While several classification algorithms based on classical image processing or d…
On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
Minh-Hao Van, Prateek Verma, Xintao Wu
Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore emergent abilitie…
In-Context Learning Demonstration Selection via Influence Analysis
Vinay M. S., Minh-Hao Van, Xintao Wu
Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates. Despite its advantages,…
Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model
Minh-Hao Van, Xintao Wu
Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore more emergent abi…
Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
Alycia N. Carey, Minh-Hao Van, Xintao Wu
How to properly set the privacy parameter in differential privacy (DP) has been an open question in DP research since it was first proposed in 2006. In this work, we demonstrate th…
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
Minh-Hao Van, Alycia N. Carey, Xintao Wu
While numerous defense methods have been proposed to prohibit potential poisoning attacks from untrusted data sources, most research works only defend against specific attacks, whi…
Poisoning Attacks on Fair Machine Learning
Minh-Hao Van, Wei Du, Xintao Wu +1
Both fair machine learning and adversarial learning have been extensively studied. However, attacking fair machine learning models has received less attention. In this paper, we pr…