# RIE-SenseNet: Riemannian Manifold Embedding of Multi-Source Industrial Sensor Signals for Robust Pattern Recognition

arXiv: https://arxiv.org/abs/2502.02428 · HTML: https://mlsanity.com/paper/2502.02428/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text](https://mlsanity.com/paper/2012.04698/) (2020)
- [Universal approximation property of invertible neural networks](https://mlsanity.com/paper/2204.07415/) (2022)
- [Diffeomorphic Counterfactuals with Generative Models](https://mlsanity.com/paper/2206.05075/) (2022)
- [Flow-based Generative Models for Learning Manifold to Manifold Mappings](https://mlsanity.com/paper/2012.10013/) (2020)
- [DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection](https://mlsanity.com/paper/2409.01604/) (2024)
- [Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an example](https://mlsanity.com/paper/2409.02546/) (2024)

## Abstract (arXiv)

Industrial sensor networks produce complex signals with nonlinear structure and shifting distributions. We propose RIE-SenseNet, a novel geometry-aware Transformer model that embeds sensor data in a Riemannian manifold to tackle these challenges. By leveraging hyperbolic geometry for sequence modeling and introducing a manifold-based augmentation technique, RIE-SenseNet preserves sensor signal structure and generates realistic synthetic samples. Experiments show RIE-SenseNet achieves >90% F1-score, far surpassing CNN and Transformer baselines. These results illustrate the benefit of combining non-Euclidean feature representations with geometry-consistent data augmentation for robust pattern recognition in industrial sensing.