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5 results for “anomaly detection”
Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
Researchers introduced ORCA, a two-stage contrastive learning framework for anomaly detection in collider physics that improves sensitivity to new physics signals and enables interpretable attribution of anomalies to known physics processes using embedding geometry.
Aug 17, 2026
UrgenT Help Detecting Performance Regressions Using Machine Learning and Hardware Counters [P]
A Reddit user seeks community advice on methodological best practices for one-class anomaly detection in performance regression testing using hardware counters, with limited healthy-sample data.
Aug 14, 2026
ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models
ChronoSSM is a new autoregressive State Space Model that jointly trains on both event tokens and timestamps to improve temporal reasoning in sequence modeling, addressing a gap where timing is typically treated as secondary to event prediction.
Aug 12, 2026
Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs
Researchers propose an FPGA-optimized Transformer architecture for real-time financial time-series outlier detection, aiming to improve speed and stability of downstream data processing.
Jul 28, 2026
IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery
IonSense-QKG is a metadata framework that adds quantum-readiness attributes to public lithium-ion battery datasets to help researchers identify which datasets are technically suitable for near-term hybrid quantum-classical ML workflows.
Published Jul 3, 2026 · Analyzed Jul 6, 2026