9 citations · 29 across the 9 of their papers we have counts for
9 papers
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models
Cristina N. Vasconcelos, Abdullah Rashwan, Austin Waters +22
We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable trainin…
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
Ruohong Zhang, Liangke Gui, Zhiqing Sun +8
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However,…
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
Xin Yuan, Jinoo Baek, Keyang Xu +2
We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capt…
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond
Zhecan Wang, Long Chen, Haoxuan You +6
Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. However, we have identified two dataset biases t…
Unsupervised Out-of-Domain Detection via Pre-trained Transformers
Keyang Xu, Tongzheng Ren, Shikun Zhang +2
Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outp…
A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media
Rahul Radhakrishnan Iyer, Jing Chen, Haonan Sun +1
Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace. With such adoption, it is increasingly becoming a rich source of data for op…
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
Xiangan Liu, Keyang Xu, Pengtao Xie +1
Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs). However, the training of a supervised model requires disease-sp…
Multimodal Machine Learning for Automated ICD Coding
Keyang Xu, Mike Lam, Jingzhi Pang +9
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modali…
A Structure-Oriented Unsupervised Crawling Strategy for Social Media Sites
Keyang Xu, Kyle Yingkai Gao, Jamie Callan
Existing techniques for efficiently crawling social media sites rely on URL patterns, query logs, and human supervision. This paper describes SOUrCe, a structure-oriented unsupervi…