A smoothed Monte-Carlo estimator of level-crossing density, derived via the co-area formula, serves as a grid-free auxiliary loss for INRs, matching but not beating frequency-domain baselines on natural images and winning on homogeneous textures.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
Courant is a state-adaptive Perceiver encoder-processor-decoder surrogate trained with L2 loss that yields interpretable, multiscale, locally supported latent features acting as time-evolving spatial basis functions.
Refined DHS targets, two-stage image-quality screening, and spherical-harmonic geo-encoding reduce KidSat MAE from 0.2167 to 0.1759 (18.83 percent relative) and reach 0.1658 on 33 African countries.
A neural network predicts sensitive pseudospectra regions from matrix features to accelerate computation on structured non-normal banded matrices while preserving accuracy in identifying those regions.
citing papers explorer
-
Level-Crossing Density as a Mesh-Free High-Frequency Auxiliary Loss for Implicit Neural Representations
A smoothed Monte-Carlo estimator of level-crossing density, derived via the co-area formula, serves as a grid-free auxiliary loss for INRs, matching but not beating frequency-domain baselines on natural images and winning on homogeneous textures.
-
Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition
Courant is a state-adaptive Perceiver encoder-processor-decoder surrogate trained with L2 loss that yields interpretable, multiscale, locally supported latent features acting as time-evolving spatial basis functions.
-
Enhancing the KidSat Model: Integrating Geographical Encoding and Data Quality Assessment for Childhood Poverty Prediction
Refined DHS targets, two-stage image-quality screening, and spherical-harmonic geo-encoding reduce KidSat MAE from 0.2167 to 0.1759 (18.83 percent relative) and reach 0.1658 on 33 African countries.
-
Neural-Guided Domain Restriction to Accelerate Pseudospectra Computation for Structured Non-normal Banded Matrices
A neural network predicts sensitive pseudospectra regions from matrix features to accelerate computation on structured non-normal banded matrices while preserving accuracy in identifying those regions.