27 citations · 32 across the 15 of their papers we have counts for
15 papers
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,…
Neural Approximation and Its Applications
Wei-Hao Wu, Ting-Zhu Huang, Xi-Le Zhao +2
Multivariate function approximation is a fundamental problem in machine learning. Classic multivariate function approximations rely on hand-crafted basis functions (e.g., polynomia…
Neural Operator-Grounded Continuous Tensor Function Representation and Its Applications
Ruoyang Su, Xi-Le Zhao, Sheng Liu +3
Recently, continuous tensor functions have attracted increasing attention, because they can unifiedly represent data both on mesh grids and beyond mesh grids. However, since mode-$…
Beyond Low-rankness: Guaranteed Matrix Recovery via Modified Nuclear Norm
Jiangjun Peng, Yisi Luo, Xiangyong Cao +2
The nuclear norm (NN) has been widely explored in matrix recovery problems, such as Robust PCA and matrix completion, leveraging the inherent global low-rank structure of the data.…
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…
Motion-Enhanced Nonlocal Similarity Implicit Neural Representation for Infrared Dim and Small Target Detection
Pei Liu, Yisi Luo, Wenzhen Wang +1
Infrared dim and small target detection presents a significant challenge due to dynamic multi-frame scenarios and weak target signatures in the infrared modality. Traditional low-r…
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…
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
Jiayi Li, Jinyu Xie, Yisi Luo +2
Recently, tensor singular value decomposition (t-SVD) has emerged as a promising tool for hyperspectral image (HSI) processing. In the t-SVD, there are two key building blocks: (i)…
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning
Zitai Xu, Yisi Luo, Bangyu Wu +1
Seismic data often undergoes severe noise due to environmental factors, which seriously affects subsequent applications. Traditional hand-crafted denoisers such as filters and regu…
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
Yisi Luo, Xile Zhao, Zhemin Li +2
Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become on…