1 citations · 1 across the 10 of their papers we have counts for
11 papers
Pre-Trained Low-Rank Tensor Decomposition for Multi-Dimensional Image Recovery
Bing-Zhang Fu, Zhi-Long Han, Ting-Zhu Huang +2
Recently, tensor decompositions are prevalent for multi-dimensional image representation, which learn the instance-specific structure of each image from scratch. However, tensor de…
Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification
Zirui Deng, Jingbo Sun, Deyu Meng +1
Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-co…
A Distributional View for Visual Mechanistic Interpretability: KL-Minimal Soft-Constraint Principle
Guancheng Zhou, Yisi Luo, Zhengfu He +5
Most current paradigms in visual mechanistic interpretability (MI) remain confined to interpreting internal units of the vision model via heuristic methods (e.g., top- activatio…
Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel
Ruihua Chen, Yisi Luo, Bangyu Wu +2
Full-waveform inversion (FWI) estimates unknown parameters in the wave equation from limited boundary measurements. Recent advances in neural reparameterized FWI (NeurFWI) demonstr…
Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework
Ruihua Chen, Yisi Luo, Bangyu Wu +1
Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements and has been widely applied in geophysical exploration, medical imaging,…
Tensor Decomposition Structure Search Framework from an Interaction Perspective
Ting-Wei Zhou, Xi-Le Zhao, Sheng Liu +3
Recently, tensor decompositions have attracted increasing attention. Fundamentally, different interactions among factors induce distinct tensor decomposition structures (i.e., tens…
Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encoding
Zhenyu Jin, Yisi Luo, Xile Zhao +1
Compressive imaging (CI) reconstruction, such as snapshot compressive imaging (SCI) and compressive sensing magnetic resonance imaging (MRI), aims to recover high-dimensional image…
Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives
Yisi Luo, Xile Zhao, Deyu Meng
Recently, continuous representation methods emerge as novel paradigms that characterize the intrinsic structures of real-world data through function representations that map positi…
Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters
Chang Yu, Yisi Luo, Kai Ye +2
Implicit neural representation (INR) has emerged as a powerful paradigm for visual data representation. However, classical INR methods represent data in the original space mixed wi…
NeurTV: Total Variation on the Neural Domain
Yisi Luo, Xile Zhao, Kai Ye +1
Recently, we have witnessed the success of total variation (TV) for many imaging applications. However, traditional TV is defined on the original pixel domain, which limits its pot…
Revisiting Nonlocal Self-Similarity from Continuous Representation
Yisi Luo, Xile Zhao, Deyu Meng
Nonlocal self-similarity (NSS) is an important prior that has been successfully applied in multi-dimensional data processing tasks, e.g., image and video recovery. However, existin…