Detection

SHAP KernelExplainer takes ~30 ms per prediction (even with a small background) A neuro-symbolic model generates explanations contained in the forward pass in 0.9 ms That’s a 33× speedup with deterministic outputs Fraud recall...

, every metric looked perfect.RWSS = 1.000. Output probabilities unchanged. No labels moved.All the pieces said “all clear.” Then the alert fired anyway. Window 3: severity=warning RWSS=1.000 fired=True ← FIDI Z fires here The model’s predictions didn’t...

Abstract datasets are extremely imbalanced, with positive rates below 0.2%. Standard neural networks trained with weighted binary cross-entropy often achieve high ROC-AUC but struggle to discover suspicious transactions under threshold-sensitive metrics. I propose a...

Introduction: Why this text was created. Anomaly detection: Quick overview. Image size: Is a bigger input size value it? Center crop: Concentrate on the article. Background removal: Remove all you don’t need. Early stopping: Use a validation set. Conclusion 1. Introduction There...

Feature detection is a website of computer vision that focuses on using tools to detect regions of interest in images. A big aspect of most feature detection algorithms is that they don't employ machine...

was co-authored by Sebastian Humberg and Morris Stallmann. Introduction      Machine learning (ML) models are designed to make accurate predictions based on patterns in historical data. But what if these patterns change overnight? For...

fascinating points of time series is the intrinsic complexity of such an apparently easy kind of information. At the tip of the day, in time series, you've an x axis that typically represents time...

Introduction can we discover latent groups of patients in a big cohort? How can we discover similarities amongst patients that transcend the well-known comorbidity clusters related to specific diseases? And more importantly, how can...