2 citations · 4 across the 11 of their papers we have counts for
12 papers
VPFlow: Value-Predictive Planning with Imperfect World Models for Offline Goal Reaching
Yunzhe Hu, Dong Xu
Offline goal-conditioned reinforcement learning enables skill acquisition by reaching any goal from any state without dense rewards. Model-based methods allow for planning by scali…
Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation
Zekun Li, Yinghuan Shi, Yang Gao +1
Diffusion-based data augmentation (DiffDA) has emerged as a promising approach to improving classification performance under data scarcity. However, existing works vary significant…
OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model
Maomao Li, Zhen Li, Kaipeng Zhang +3
Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual prompts. Benefiting from the rapid advanc…
Learning Diffusion Policy from Primitive Skills for Robot Manipulation
Zhihao Gu, Ming Yang, Difan Zou +1
Diffusion policies (DP) have recently shown great promise for generating actions in robotic manipulation. However, existing approaches often rely on global instructions to produce…
Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems
Chuyue Lou, M. Amine Atoui
Recently, fault diagnosis methods for marine machinery systems based on deep learning models have attracted considerable attention in the shipping industry. Most existing studies a…
SoftCFG: Uncertainty-guided Stable Guidance for Visual Autoregressive Model
Dongli Xu, Aleksei Tiulpin, Matthew B. Blaschko
Autoregressive (AR) models have emerged as powerful tools for image generation by modeling images as sequences of discrete tokens. While Classifier-Free Guidance (CFG) has been ado…
SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs via Stable Safety-critical Expert Identification
Zhenglin Lai, Mengyao Liao, Bingzhe Wu +5
Large language models with Mixture-of-Experts (MoE) architectures achieve efficiency and scalability, yet their routing mechanisms introduce safety alignment challenges insufficien…
ConSCompF: Consistency-focused Similarity Comparison Framework for Generative Large Language Models
Alexey Karev, Dong Xu
Large language models (LLMs) have been one of the most important discoveries in machine learning in recent years. LLM-based artificial intelligence (AI) assistants, such as ChatGPT…
Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization
Yunzhe Hu, Difan Zou, Dong Xu
Transformer-based models have achieved remarkable success, but their core components, Transformer layers, are largely heuristics-driven and engineered from the bottom up, calling f…
An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models
Yunzhe Hu, Difan Zou, Dong Xu
Deep neural networks have long been criticized for being black-box. To unveil the inner workings of modern neural architectures, a recent work \cite{yu2024white} proposed an inform…
Improving Long-Text Alignment for Text-to-Image Diffusion Models
Luping Liu, Chao Du, Tianyu Pang +3
The rapid advancement of text-to-image (T2I) diffusion models has enabled them to generate unprecedented results from given texts. However, as text inputs become longer, existing e…
Gear Training: A new way to implement high-performance model-parallel training
Hao Dong, Shuai Li, Dongchang Xu +2
The training of Deep Neural Networks usually needs tremendous computing resources. Therefore many deep models are trained in large cluster instead of single machine or GPU. Though…