Active Learning

Ceyeborg’s neural networks are equipped with advanced measures for uncertainty detection, ensuring thorough monitoring and recording of critical anomalies. These capabilities are essential for tracing events, logging failures, and promptly identifying unintended changes within your facility or production processes. Anomalies are automatically captured, labeled, and systematically integrated into subsequent iterations of our neural networks through continuous training processes. This iterative refinement not only enhances the network’s accuracy but also fortifies its ability to adapt and respond effectively to evolving operational conditions.

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Active Learning

Our AI-based monitoring systems employ advanced data classification and sorting techniques, leveraging learned representations from real-world data. The algorithms measure uncertainty by probing the entropy in their output logits or employing Bayesian methods to estimate posterior distributions over model parameters. High entropy or variance in these outputs indicates low confidence in predictions, flagging exceptional cases for human review.

Anomalies are detected through various techniques, such as deviation from established patterns or statistical norms, clustering-based outlier detection, and reconstruction errors in autoencoders. These anomalies are then automatically captured and labeled, feeding into a continuous active learning cycle where the model is retrained with this new data. This process ensures the system remains up-to-date and proficient at identifying rare or unseen events.

By systematically integrating these sophisticated uncertainty measures and anomaly detection techniques, Ceyeborg’s neural networks provide robust and reliable monitoring solutions and reactive systems through our custom electronic control units (ECUs). This active learning framework continuously refines the model’s understanding, improving its performance and maintaining high accuracy in detecting and responding to operational anomalies.

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