{"list":"other","total":57,"rows":[{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"2303.01469","title":"Consistency Models","created":"2023-03-02","cited_by_count":1822},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"1906.04032","title":"Neural Spline Flows","created":"2019-06-10","cited_by_count":596},{"id":"1911.01429","title":"The frontier of simulation-based inference","created":"2019-11-04","cited_by_count":502},{"id":"1810.09538","title":"Pyro: Deep Universal Probabilistic Programming","created":"2018-10-18","cited_by_count":380},{"id":"2112.10510","title":"Transformers Can Do Bayesian Inference","created":"2021-12-20","cited_by_count":360},{"id":"1905.07488","title":"Automatic Posterior Transformation for Likelihood-Free Inference","created":"2019-05-17","cited_by_count":219},{"id":"1912.11554","title":"Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro","created":"2019-12-24","cited_by_count":191},{"id":"2101.04653","title":"Benchmarking Simulation-Based Inference","created":"2021-01-12","cited_by_count":162},{"id":"2003.06281","title":"BayesFlow: Learning complex stochastic models with invertible neural networks","created":"2020-03-13","cited_by_count":144},{"id":"2208.01893","title":"Flow Annealed Importance Sampling Bootstrap","created":"2022-08-03","cited_by_count":127},{"id":"1804.06788","title":"Validating Bayesian Inference Algorithms with Simulation-Based Calibration","created":"2018-04-18","cited_by_count":124},{"id":"2305.17161","title":"Flow Matching for Scalable Simulation-Based Inference","created":"2023-05-26","cited_by_count":101},{"id":"1903.08008","title":"Rank-normalization, folding, and localization: An improved $\\widehat{R}$ for assessing convergence of MCMC","created":"2019-03-19","cited_by_count":93},{"id":"2102.07501","title":"Annealed Flow Transport Monte Carlo","created":"2021-02-15","cited_by_count":90},{"id":"1903.03704","title":"NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport","created":"2019-03-09","cited_by_count":74},{"id":"2404.09636","title":"All-in-one simulation-based inference","created":"2024-04-15","cited_by_count":74},{"id":"2112.03235","title":"Simulation Intelligence: Towards a New Generation of Scientific Methods","created":"2021-12-06","cited_by_count":68},{"id":"1802.02538","title":"Yes, but Did It Work?: Evaluating Variational Inference","created":"2018-02-07","cited_by_count":53},{"id":"2210.04872","title":"Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models","created":"2022-10-10","cited_by_count":50},{"id":"2011.01808","title":"Bayesian Workflow","created":"2020-11-03","cited_by_count":49},{"id":"2112.08866","title":"Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks","created":"2021-12-16","cited_by_count":49},{"id":"2209.14249","title":"Compositional Score Modeling for Simulation-based Inference","created":"2022-09-28","cited_by_count":48},{"id":"2305.15871","title":"Learning Robust Statistics for Simulation-based Inference under Model Misspecification","created":"2023-05-25","cited_by_count":48},{"id":"2210.06564","title":"Robust Neural Posterior Estimation and Statistical Model Criticism","created":"2022-10-12","cited_by_count":46},{"id":"2203.04176","title":"Variational methods for simulation-based inference","created":"2022-03-08","cited_by_count":43},{"id":"1507.02646","title":"Pareto Smoothed Importance Sampling","created":"2015-07-09","cited_by_count":42},{"id":"2404.12484","title":"Neural Methods for Amortized Inference","created":"2024-04-18","cited_by_count":39},{"id":"2103.01085","title":"Challenges and Opportunities in High-dimensional Variational Inference","created":"2021-03-01","cited_by_count":35},{"id":"2306.16015","title":"BayesFlow: Amortized Bayesian Workflows With Neural Networks","created":"2023-06-28","cited_by_count":32},{"id":"2208.13624","title":"Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation","created":"2022-08-29","cited_by_count":28},{"id":"2210.05686","title":"Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference","created":"2022-10-11","cited_by_count":28},{"id":"2103.10522","title":"Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparison","created":"2021-03-18","cited_by_count":26},{"id":"2108.03782","title":"Pathfinder: Parallel quasi-Newton variational inference","created":"2021-08-09","cited_by_count":26},{"id":"2207.11640","title":"Reliable amortized variational inference with physics-based latent distribution correction","created":"2022-07-24","cited_by_count":25},{"id":"2006.08655","title":"Variational Bayesian Monte Carlo with Noisy Likelihoods","created":"2020-06-15","cited_by_count":23},{"id":"2312.05440","title":"Consistency Models for Scalable and Fast Simulation-Based Inference","created":"2023-12-09","cited_by_count":22},{"id":"2302.04763","title":"On Sampling with Approximate Transport Maps","created":"2023-02-09","cited_by_count":21},{"id":"2310.11122","title":"Sensitivity-Aware Amortized Bayesian Inference","created":"2023-10-17","cited_by_count":20},{"id":"2211.02383","title":"Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity","created":"2022-11-04","cited_by_count":19},{"id":"2404.07593","title":"Diffusion posterior sampling for simulation-based inference in tall data settings","created":"2024-04-11","cited_by_count":19},{"id":"2010.00300","title":"OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany","created":"2020-10-01","cited_by_count":18},{"id":"2306.03580","title":"L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference","created":"2023-06-06","cited_by_count":18},{"id":"2205.15784","title":"Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization","created":"2022-05-31","cited_by_count":15},{"id":"2310.04395","title":"Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference","created":"2023-10-06","cited_by_count":14},{"id":"2210.10644","title":"Transport Elliptical Slice Sampling","created":"2022-10-19","cited_by_count":12},{"id":"2307.13918","title":"Simulation-based Inference for Cardiovascular Models","created":"2023-07-26","cited_by_count":11},{"id":"2405.05398","title":"ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems","created":"2024-05-08","cited_by_count":11},{"id":"2110.13017","title":"Nested $\\hat R$: Assessing the convergence of Markov chain Monte Carlo when running many short chains","created":"2021-10-25","cited_by_count":9},{"id":"2405.14392","title":"Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows","created":"2024-05-23","cited_by_count":9},{"id":"2407.18648","title":"Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation","created":"2024-07-26","cited_by_count":4},{"id":"2302.12419","title":"A Targeted Accuracy Diagnostic for Variational Approximations","created":"2023-02-24","cited_by_count":1}],"next_cursor":null}