SAGE uses simplex-anchored graph-state equipartition and equiangular tight frames to perform structural inference on representations, bypassing distribution estimation in universal semi-supervised learning and achieving 8.52% average accuracy gains on benchmarks.
International Conference on Machine Learning (ICML) , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
TinyBayes delivers 78.7% accuracy on cocoa disease detection with a 9.5 MB edge pipeline that uses YOLOv8-Nano, MobileNetV3-Small, and a closed-form Jacobi-DMR Bayesian classifier.
citing papers explorer
-
Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning
SAGE uses simplex-anchored graph-state equipartition and equiangular tight frames to perform structural inference on representations, bypassing distribution estimation in universal semi-supervised learning and achieving 8.52% average accuracy gains on benchmarks.
-
TinyBayes: Closed-Form Bayesian Inference via Jacobi Prior for Real-Time Image Classification on Edge Devices
TinyBayes delivers 78.7% accuracy on cocoa disease detection with a 9.5 MB edge pipeline that uses YOLOv8-Nano, MobileNetV3-Small, and a closed-form Jacobi-DMR Bayesian classifier.