PROBE structures runtime telemetry into diagnoses and evidence-grounded guidance, raising recovery rates by 12.45 points over baselines on 257 unresolved software repair and AIOps cases.
IEEE 109, 5 (May 2021), 756–795
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SIC is a prior-fitted network that amortizes Bayesian survival inference by pretraining on synthetic data generated from a controllable survival prior, delivering competitive or better performance than classical and deep models on real datasets especially in small-sample regimes.
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
RoseCDL adds stochastic windowing and inline outlier detection to convolutional dictionary learning to enable scalable unsupervised anomaly detection via local reconstruction loss on large signals.
citing papers explorer
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Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
PROBE structures runtime telemetry into diagnoses and evidence-grounded guidance, raising recovery rates by 12.45 points over baselines on 257 unresolved software repair and AIOps cases.
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Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
SIC is a prior-fitted network that amortizes Bayesian survival inference by pretraining on synthetic data generated from a controllable survival prior, delivering competitive or better performance than classical and deep models on real datasets especially in small-sample regimes.
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On Diffusion Modeling for Anomaly Detection
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
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RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection
RoseCDL adds stochastic windowing and inline outlier detection to convolutional dictionary learning to enable scalable unsupervised anomaly detection via local reconstruction loss on large signals.
- KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis