arXiv (All)
open access
Neural Conjugate Aggregation: Identifiable Unsupervised Multi-Sensor Regression under Heterogeneous Sensor Bias
We study regression-based data fusion under uncertainty, where multiple noisy and biased measurement sources are available but ground-truth labels are absent during training. This setting arises in sensor networks, simulation ensembles, and scientific monitoring systems where supervision is costly o
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Overlapping Covariance Intersection: Fusion with Partial Structural Knowledge of Correlation from Multiple Sources#1156304
- DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning#972887
- Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference#343423
- OmicsTransformer: self-supervised masked consistency and uncertainty-aware fusion for robust multi-omics prediction.#388758
- FedCARE: Fuzzy-Supervised Federated Inference with Confidence Gating for Resilient IIoT Sensor Networks.#318062
Record · ID 1248485
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive