10 papers
MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration
Jianmin Liao, Lei Huang, Ronglong Fang +3
MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building o…
Oracle Supervision Transfers for Hyperparameter Prediction in Model-Based Image Denoising
Jianmin Liao, Lixin Shen, Yuesheng Xu
Hyperparameter prediction is a critical practical bottleneck for model-based image denoisers, ranging from classical TV/TGV variational solvers to modern diffusion-based models suc…
Multigrade Neural Network Approximation
Shijun Zhang, Zuowei Shen, Yuesheng Xu
We study multigrade deep learning (MGDL) as a principled framework for structured error refinement in deep neural networks. While the approximation power of neural networks is now…
Online Learning of HTN Methods for integrated LLM-HTN Planning
Yuesheng Xu, Hector Munoz-Avila
We present online learning of Hierarchical Task Network (HTN) methods in the context of integrated HTN planning and LLM-based chatbots. Methods indicate when and how to decompose t…
Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
Ronglong Fang, Yuesheng Xu
Multi-grade deep learning (MGDL) has been shown to significantly outperform the standard single-grade deep learning (SGDL) across various applications. This work aims to investigat…
Sparsity-Guided Multi-Parameter Selection in -Regularized Models via a Fixed-Point Proximity Approach
Qianru Liu, Rui Wang, Yuesheng Xu
We study a regularization framework that combines a convex fidelity term with multiple -based regularizers, each linked to a distinct linear transform. This multi-penalty m…