Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Proposes bearing-wise data partitioning to remove leakage in ML bearing fault diagnosis, reformulates as multi-label classification, and shows training bearing count drives generalization on four public datasets.
A trial of the ROAR protocol shows a multi-agent system using forkable statistical microservices with economic self-interest for micro-predictions, with lessons reported and a Prediction Web suggested.
citing papers explorer
-
Neural Point-Forms
Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
-
Towards a more realistic evaluation of machine learning models for bearing fault diagnosis
Proposes bearing-wise data partitioning to remove leakage in ML bearing fault diagnosis, reformulates as multi-label classification, and shows training bearing count drives generalization on four public datasets.
-
Self Organizing Supply Chains for Micro-Prediction: Present and Future uses of the ROAR Protocol
A trial of the ROAR protocol shows a multi-agent system using forkable statistical microservices with economic self-interest for micro-predictions, with lessons reported and a Prediction Web suggested.