Spectral Higher-Order Neural Networks
Spectral Higher-Order Neural Networks explores A new algorithmic strategy to incorporate higher-order interactions in general-purpose, feedforward, network structures by leveraging spectral attributes.. Commercial viability score: 2/10 in Neural Network Architectures.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisGeneralization Bounds for Spectral GNNs via Fourier Domain AnalysisViability: 1.0
- Prior WorkSpectral Superposition: A Theory of Feature GeometryViability: 2.0
- Higher ViabilityPosition: Spectral GNNs Are Neither Spectral Nor Superior for Node ClassificationViability: 3.0
- Higher ViabilityKirchhoff-Inspired Neural Networks for Evolving High-Order PerceptionViability: 4.0
- Higher ViabilitySCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation LearningViability: 7.0