Sequence Search uses neural architecture search and a differentiable Bloch simulator to automatically create and optimize MRI pulse sequences that satisfy given design goals.
Knowledge- based systems212, 106622 (2021)
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An LM-guided counterfactual pipeline recommends minimal ordinal changes to communication features like tone and actionability, yielding a mean +6.41% gain in predicted positive feedback under independent auditor models.
yvsoucom-iterkit shows that performance on two healthcare datasets is dominated by a small subset of interacting pipeline components, allowing constrained search spaces to improve efficiency, stability, and interpretability.
An evolutionary search framework auto-configures multi-scale bi-branch CNNs to generate Pareto fronts of error-versus-complexity models for multi-output time-series forecasting.
FLIM-BoFP replaces per-block patch clustering in FLIM networks with a single input-level clustering step that creates a bag of feature points used to define filters across all encoder blocks, yielding faster training for parasite detection in optical microscopy.
citing papers explorer
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Sequence Search: Automated Sequence Design using Neural Architecture Search
Sequence Search uses neural architecture search and a differentiable Bloch simulator to automatically create and optimize MRI pulse sequences that satisfy given design goals.
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Improving Medical Communication using Rubric-Guided Counterfactual Recommendations
An LM-guided counterfactual pipeline recommends minimal ordinal changes to communication features like tone and actionability, yielding a mean +6.41% gain in predicted positive feedback under independent auditor models.
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A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
yvsoucom-iterkit shows that performance on two healthcare datasets is dominated by a small subset of interacting pipeline components, allowing constrained search spaces to improve efficiency, stability, and interpretability.
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Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecasting
An evolutionary search framework auto-configures multi-scale bi-branch CNNs to generate Pareto fronts of error-versus-complexity models for multi-output time-series forecasting.
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FLIM Networks with Bag of Feature Points
FLIM-BoFP replaces per-block patch clustering in FLIM networks with a single input-level clustering step that creates a bag of feature points used to define filters across all encoder blocks, yielding faster training for parasite detection in optical microscopy.