Simulation-based Deep Sets model with neural posterior estimation halves scatter in cluster mass estimates from galaxy kinematics compared to the M-sigma relation.
Pyro: Deep Universal Probabilistic Programming
9 Pith papers cite this work, alongside 591 external citations. Polarity classification is still indexing.
9
Pith papers citing it
591
external citations · Pith
abstract
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.
representative citing papers
A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
Combined five-PTA dataset yields posterior on SGWB power-law amplitude and index consistent with nonzero signal but below 5-sigma significance, with reconstructed angular correlations matching the Hellings-Downs prediction.
GRPO can teach a 3B language model to emit executable Prolog, but the highest-accuracy models often hardcode answers instead of reasoning in Prolog, producing an accuracy–auditability trade-off.
Spectral-siren H0 constraints from GWTC-4.0 binary black holes remain robust when the mass spectrum is permitted to evolve with redshift at current detector sensitivity.
A new compact hierarchical triple main-sequence star system G1010 was discovered through combined low- and high-SNR spectroscopy, Gaia DR3 data, and TESS light curve analysis, showing an inner eclipsing binary rather than a compact object companion.
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.
Derives stellar labels for 357k RVS giants via The Cannon and uses abundance-based logistic regression to tag GSE debris with consistent patterns after kinematic filtering.
citing papers explorer
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Cluster Mass Inference from Galaxy Kinematics
Simulation-based Deep Sets model with neural posterior estimation halves scatter in cluster mass estimates from galaxy kinematics compared to the M-sigma relation.
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Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
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Stochastic gravitational-wave background search using data from five pulsar timing arrays
Combined five-PTA dataset yields posterior on SGWB power-law amplitude and index consistent with nonzero signal but below 5-sigma significance, with reconstructed angular correlations matching the Hellings-Downs prediction.
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Training Language Models to Use Prolog as a Tool
GRPO can teach a 3B language model to emit executable Prolog, but the highest-accuracy models often hardcode answers instead of reasoning in Prolog, producing an accuracy–auditability trade-off.
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Gravitational-wave constraints on $H_0$ are robust to (putative) redshift evolution in the binary black hole mass spectrum at current sensitivity
Spectral-siren H0 constraints from GWTC-4.0 binary black holes remain robust when the mass spectrum is permitted to evolve with redshift at current detector sensitivity.
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Discovery of a compact hierarchical triple main-sequence star system while searching for binary stars with compact objects
A new compact hierarchical triple main-sequence star system G1010 was discovered through combined low- and high-SNR spectroscopy, Gaia DR3 data, and TESS light curve analysis, showing an inner eclipsing binary rather than a compact object companion.
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Symbolic Emulators for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.
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The GALAH Survey: Neutron-Capture Elemental Abundances for 350,000 Gaia-RVS Spectra and the Chemodynamics of Accreted Structures
Derives stellar labels for 357k RVS giants via The Cannon and uses abundance-based logistic regression to tag GSE debris with consistent patterns after kinematic filtering.
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