Conditional normalizing flows approximate intractable likelihoods arising from cell division history to conclude that glc3 is mostly inactive under nutrient stress in yeast, with brief transient expression.
A trust crisis in simulation-based inference? your posterior approximations can be unfaithful
9 Pith papers cite this work, alongside 35 external citations. Polarity classification is still indexing.
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Amortized neural posterior estimation reproduces nested sampling constraints on RMF couplings for neutron-star EOS with no bias and generates 30,000 samples in 2.5 seconds.
Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
A multimodal amortized neural posterior estimator trained on realistic simulations recovers DEB parameters accurately with calibrated uncertainties on held-out tests.
Semi-supervised DL anomaly detector (VAE + classifier) for model-independent searches in DARWIN, outperforming classical likelihood tests on simulated WIMP injections while learning directly from raw high-dimensional outputs.
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.
citing papers explorer
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Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history
Conditional normalizing flows approximate intractable likelihoods arising from cell division history to conclude that glc3 is mostly inactive under nutrient stress in yeast, with brief transient expression.
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Amortized Simulation-Based Inference of Relativistic Mean-Field Couplings for Neutron-Star Equations of State
Amortized neural posterior estimation reproduces nested sampling constraints on RMF couplings for neutron-star EOS with no bias and generates 30,000 samples in 2.5 seconds.
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Towards Practical Field-Level Inference for Weak Lensing
Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.
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Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
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Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problems
Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
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Neural Simulation-based Inference with Hierarchical Priors for Detached Eclipsing Binaries
A multimodal amortized neural posterior estimator trained on realistic simulations recovers DEB parameters accurately with calibrated uncertainties on held-out tests.
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Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline
Semi-supervised DL anomaly detector (VAE + classifier) for model-independent searches in DARWIN, outperforming classical likelihood tests on simulated WIMP injections while learning directly from raw high-dimensional outputs.
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Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
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A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios
A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.