9 papers
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,…
TenExp: Mixture-of-Experts-Based Tensor Decomposition Structure Search Framework
Ting-Wei Zhou, Xi-Le Zhao, Sheng Liu +3
Recently, tensor decompositions continue to emerge and receive increasing attention. Selecting a suitable tensor decomposition to exactly capture the low-rank structures behind the…
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…