Pruning AMR: Efficient Visualization of Implicit Neural Representations via Weight Matrix Analysis
Fuente:
arXiv
Guardado en:
| Autores principales: | Zvonek, Jennifer, Gillette, Andrew |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
ContHutch++: Stochastic trace estimation for implicit integral operators
por: Zvonek, Jennifer, et al.
Publicado: (2023)
por: Zvonek, Jennifer, et al.
Publicado: (2023)
Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machines
por: Cho, Woojin, et al.
Publicado: (2026)
por: Cho, Woojin, et al.
Publicado: (2026)
Convergence and Sketching-Based Efficient Computation of Neural Tangent Kernel Weights in Physics-Based Loss
por: Hirsch, Max, et al.
Publicado: (2025)
por: Hirsch, Max, et al.
Publicado: (2025)
Unified Stochastic Framework for Neural Network Quantization and Pruning
por: Zhang, Haoyu, et al.
Publicado: (2024)
por: Zhang, Haoyu, et al.
Publicado: (2024)
Spectral Analysis of the Weighted Frobenius Objective
por: Trifonov, Vladislav, et al.
Publicado: (2025)
por: Trifonov, Vladislav, et al.
Publicado: (2025)
Neural Parameter Regression for Explicit Representations of PDE Solution Operators
por: Mundinger, Konrad, et al.
Publicado: (2024)
por: Mundinger, Konrad, et al.
Publicado: (2024)
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
por: Zhang, Rui, et al.
Publicado: (2023)
por: Zhang, Rui, et al.
Publicado: (2023)
Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
por: Baptista, Ricardo, et al.
Publicado: (2025)
por: Baptista, Ricardo, et al.
Publicado: (2025)
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations
por: Tang, Shanshan, et al.
Publicado: (2019)
por: Tang, Shanshan, et al.
Publicado: (2019)
Matrix Decomposition and Applications
por: Lu, Jun
Publicado: (2022)
por: Lu, Jun
Publicado: (2022)
On the Role of Initialization on the Implicit Bias in Deep Linear Networks
por: Gruber, Oria, et al.
Publicado: (2024)
por: Gruber, Oria, et al.
Publicado: (2024)
A Mathematical Analysis of Neural Operator Behaviors
por: Le, Vu-Anh, et al.
Publicado: (2024)
por: Le, Vu-Anh, et al.
Publicado: (2024)
Continuum Attention for Neural Operators
por: Calvello, Edoardo, et al.
Publicado: (2024)
por: Calvello, Edoardo, et al.
Publicado: (2024)
Weighted quantization using MMD: From mean field to mean shift via gradient flows
por: Belhadji, Ayoub, et al.
Publicado: (2025)
por: Belhadji, Ayoub, et al.
Publicado: (2025)
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression
por: Mango, John, et al.
Publicado: (2024)
por: Mango, John, et al.
Publicado: (2024)
Graph Neural Networks for Community Detection in Graph Signal Analysis
por: Cavoretto, Roberto, et al.
Publicado: (2026)
por: Cavoretto, Roberto, et al.
Publicado: (2026)
Operator Learning: Algorithms and Analysis
por: Kovachki, Nikola B., et al.
Publicado: (2024)
por: Kovachki, Nikola B., et al.
Publicado: (2024)
Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
por: Liang, Zhangyong
Publicado: (2026)
por: Liang, Zhangyong
Publicado: (2026)
Neural Operator: Learning Maps Between Function Spaces
por: Kovachki, Nikola, et al.
Publicado: (2021)
por: Kovachki, Nikola, et al.
Publicado: (2021)
Query Efficient Structured Matrix Learning
por: Amsel, Noah, et al.
Publicado: (2025)
por: Amsel, Noah, et al.
Publicado: (2025)
MgNO: Efficient Parameterization of Linear Operators via Multigrid
por: He, Juncai, et al.
Publicado: (2023)
por: He, Juncai, et al.
Publicado: (2023)
Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates
por: Li, Xinyu, et al.
Publicado: (2026)
por: Li, Xinyu, et al.
Publicado: (2026)
Operator SVD with Neural Networks via Nested Low-Rank Approximation
por: Ryu, J. Jon, et al.
Publicado: (2024)
por: Ryu, J. Jon, et al.
Publicado: (2024)
On the Structure of Floating-Point Noise in Batch-Invariant GPU Matrix Multiplication
por: Yashwanth, Tadisetty Sai
Publicado: (2025)
por: Yashwanth, Tadisetty Sai
Publicado: (2025)
Matrix Phylogeny: Compact Spectral Fingerprints for Trap-Robust Preconditioner Selection
por: Baek, Jinwoo
Publicado: (2025)
por: Baek, Jinwoo
Publicado: (2025)
Convolutional Surrogate for 3D Discrete Fracture-Matrix Tensor Upscaling
por: Špetlík, Martin, et al.
Publicado: (2026)
por: Špetlík, Martin, et al.
Publicado: (2026)
Weighted variation spaces and approximation by shallow ReLU networks
por: DeVore, Ronald, et al.
Publicado: (2023)
por: DeVore, Ronald, et al.
Publicado: (2023)
Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
por: Zhang, Borong, et al.
Publicado: (2022)
por: Zhang, Borong, et al.
Publicado: (2022)
Matrix-Free Least Squares Solvers: Values, Gradients, and What to Do With Them
por: Roy, Hrittik, et al.
Publicado: (2025)
por: Roy, Hrittik, et al.
Publicado: (2025)
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
por: Cai, HanQin, et al.
Publicado: (2022)
por: Cai, HanQin, et al.
Publicado: (2022)
Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution
por: Allen, Kenneth, et al.
Publicado: (2023)
por: Allen, Kenneth, et al.
Publicado: (2023)
Concatenated Matrix SVD: Compression Bounds, Incremental Approximation, and Error-Constrained Clustering
por: Shamrai, Maksym
Publicado: (2026)
por: Shamrai, Maksym
Publicado: (2026)
Approximating Matrix Functions with Deep Neural Networks and Transformers
por: Padmanabhan, Rahul, et al.
Publicado: (2026)
por: Padmanabhan, Rahul, et al.
Publicado: (2026)
How Analysis Can Teach Us the Optimal Way to Design Neural Operators
por: Le, Vu-Anh, et al.
Publicado: (2024)
por: Le, Vu-Anh, et al.
Publicado: (2024)
Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
por: Alegría, Samuel A. Cruz, et al.
Publicado: (2025)
por: Alegría, Samuel A. Cruz, et al.
Publicado: (2025)
Smoothness Adaptivity in Constant-Depth Neural Networks: Optimal Rates via Smooth Activations
por: Liu, Yuhao, et al.
Publicado: (2026)
por: Liu, Yuhao, et al.
Publicado: (2026)
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
por: Jalalian, Yasamin, et al.
Publicado: (2025)
por: Jalalian, Yasamin, et al.
Publicado: (2025)
Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
por: Celaya, Adrian, et al.
Publicado: (2025)
por: Celaya, Adrian, et al.
Publicado: (2025)
Ejemplares similares
-
ContHutch++: Stochastic trace estimation for implicit integral operators
por: Zvonek, Jennifer, et al.
Publicado: (2023) -
Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machines
por: Cho, Woojin, et al.
Publicado: (2026) -
Convergence and Sketching-Based Efficient Computation of Neural Tangent Kernel Weights in Physics-Based Loss
por: Hirsch, Max, et al.
Publicado: (2025) -
Unified Stochastic Framework for Neural Network Quantization and Pruning
por: Zhang, Haoyu, et al.
Publicado: (2024) -
Spectral Analysis of the Weighted Frobenius Objective
por: Trifonov, Vladislav, et al.
Publicado: (2025)