Hypergraph modeling of SNNs improves neuron-to-core mapping on neuromorphic hardware by exploiting hyperedge overlap and locality for better partitioning and placement than graph-based methods.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
method 2
citation-polarity summary
years
2026 5representative citing papers
Numerical extraction of scaling dimensions and OPE coefficients for 32 primary operators in the O(2) Wilson-Fisher CFT via fuzzy-sphere regularization shows agreement with bootstrap predictions.
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
Polfed.jl provides an efficient implementation of polynomially filtered Lanczos diagonalization for mid-spectrum eigenpairs in quantum many-body systems, supporting larger sizes via on-the-fly polynomial transformations and GPU acceleration.
DATO and QMDA represent substantially different assimilation paradigms with distinct advantages and limitations in interpretability, robustness, and scalability.
citing papers explorer
-
A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware
Hypergraph modeling of SNNs improves neuron-to-core mapping on neuromorphic hardware by exploiting hyperedge overlap and locality for better partitioning and placement than graph-based methods.
-
Conformal Data for the $O(2)$ Wilson-Fisher CFT in $(2+1)$-Dimensional Spacetime from Exact Diagonalization and Matrix Product States on the Fuzzy Sphere
Numerical extraction of scaling dimensions and OPE coefficients for 32 primary operators in the O(2) Wilson-Fisher CFT via fuzzy-sphere regularization shows agreement with bootstrap predictions.
-
A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
-
Computing eigenpairs of quantum many-body systems with Polfed.jl
Polfed.jl provides an efficient implementation of polynomially filtered Lanczos diagonalization for mid-spectrum eigenpairs in quantum many-body systems, supporting larger sizes via on-the-fly polynomial transformations and GPU acceleration.
-
From Classical to Quantum-Mechanical Data Assimilation: A Comparison between DATO and QMDA
DATO and QMDA represent substantially different assimilation paradigms with distinct advantages and limitations in interpretability, robustness, and scalability.