A review paper that organizes industrial visual sim-to-real literature into CAD-available, CAD-unavailable, and boundary-prior regimes based on the type of prior information available.
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks
7 Pith papers cite this work, alongside 136 external citations. Polarity classification is still indexing.
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KAPLAN-HR applies B-spline KANs to nonparametric hazard estimation in survival analysis, recovering GAMs in the single-layer case, capturing interactions via deeper layers, with convergence rates independent of covariate dimension for KAN-representable targets, and competitive performance on six cli
PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 cases.
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.
Profy uses take-level expert-amateur labels on 1083 piano recordings to produce time-aligned highlight scores that correlate with expert review points (r=0.61) on held-out amateur clips.
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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Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes
A review paper that organizes industrial visual sim-to-real literature into CAD-available, CAD-unavailable, and boundary-prior regimes based on the type of prior information available.
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KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis
KAPLAN-HR applies B-spline KANs to nonparametric hazard estimation in survival analysis, recovering GAMs in the single-layer case, capturing interactions via deeper layers, with convergence rates independent of covariate dimension for KAN-representable targets, and competitive performance on six cli
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Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 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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Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice
Profy uses take-level expert-amateur labels on 1083 piano recordings to produce time-aligned highlight scores that correlate with expert review points (r=0.61) on held-out amateur clips.
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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.