4 citations · 25 across the 13 of their papers we have counts for
15 papers
Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
Junfeng Nie, Alvin Jin, Xiaohui Chen
Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency,…
A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation
Hao-Ran Yang, Xiaohui Chen, Chuan-Xian Ren
Aiming to generalize the well-trained gaze estimation model to new target domains, Cross-domain Gaze Estimation (CDGE) is developed for real-world application scenarios. Existing C…
Multi-Modal Foundation Models for Computational Pathology: A Survey
Dong Li, Guihong Wan, Xintao Wu +7
Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopathological images. While early deve…
Large Language Model is Secretly a Protein Sequence Optimizer
Yinkai Wang, Jiaxing He, Yuanqi Du +5
We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence. Directed evolution ha…
Graph Generative Pre-trained Transformer
Xiaohui Chen, Yinkai Wang, Jiaxing He +4
Graph generation is a critical task in numerous domains, including molecular design and social network analysis, due to its ability to model complex relationships and structured da…
CompCap: Improving Multimodal Large Language Models with Composite Captions
Xiaohui Chen, Satya Narayan Shukla, Mahmoud Azab +8
How well can Multimodal Large Language Models (MLLMs) understand composite images? Composite images (CIs) are synthetic visuals created by merging multiple visual elements, such as…
Does ChatGPT and Whisper Make Humanoid Robots More Relatable?
Xiaohui Chen, Katherine Luo, Trevor Gee +1
Humanoid robots are designed to be relatable to humans for applications such as customer support and helpdesk services. However, many such systems, including Softbank's Pepper, fal…
Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis
Xiaohui Chen, Yongfei Liu, Yingxiang Yang +4
Recent advancements in text-to-image (T2I) generative models have shown remarkable capabilities in producing diverse and imaginative visuals based on text prompts. Despite the adva…
On Separate Normalization in Self-supervised Transformers
Xiaohui Chen, Yinkai Wang, Yuanqi Du +2
Self-supervised training methods for transformers have demonstrated remarkable performance across various domains. Previous transformer-based models, such as masked autoencoders (M…
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Xiaohui Chen, Jiankai Sun, Taiqing Wang +3
Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model…
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
Xiaohui Chen, Jiaxing He, Xu Han +1
Diffusion-based generative graph models have been proven effective in generating high-quality small graphs. However, they need to be more scalable for generating large graphs conta…
Interpretable Node Representation with Attribute Decoding
Xiaohui Chen, Xi Chen, Liping Liu
Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data. In this work, we systematically analyze modeling node…
NVDiff: Graph Generation through the Diffusion of Node Vectors
Xiaohui Chen, Yukun Li, Aonan Zhang +1
Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures. Recently, several works have propos…
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu +2
A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges t…
GAN Ensemble for Anomaly Detection
Xu Han, Xiaohui Chen, Li-Ping Liu
When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works apply Generative Adversarial…