AutoHGNN: Robust and Efficient Neural Architecture Search for Hypergraph Neural Networks
cs.LG, cs.AI
Submitted: 2026-09-27
Updated: 2026-09-27
Project page: http://hyperop.github.io/hyperopt
Terminology
Sources
- HNHN: Hypergraph Networks with Hyperedge Neurons
- HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs
- GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
- Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
- Instance Normalization: The Missing Ingredient for Fast Stylization
- Rethinking Architecture Selection in Differentiable NAS
- Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks