{"list":"other","total":55,"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":"2209.03003","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","created":"2022-09-07","cited_by_count":3700},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"1511.07289","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","created":"2015-11-23","cited_by_count":1070},{"id":"2303.08797","title":"Stochastic Interpolants: A Unifying Framework for Flows and Diffusions","created":"2023-03-15","cited_by_count":939},{"id":"1606.05328","title":"Conditional Image Generation with PixelCNN Decoders","created":"2016-06-16","cited_by_count":862},{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252},{"id":"1909.13189","title":"Learning Sparse Nonparametric DAGs","created":"2019-09-29","cited_by_count":207},{"id":"1908.10962","title":"Optimal transport mapping via input convex neural networks","created":"2019-08-28","cited_by_count":206},{"id":"1908.05164","title":"Unconstrained Monotonic Neural Networks","created":"2019-08-14","cited_by_count":92},{"id":"2109.12004","title":"Entropic estimation of optimal transport maps","created":"2021-09-24","cited_by_count":90},{"id":"2010.10436","title":"VarGrad: A Low-Variance Gradient Estimator for Variational Inference","created":"2020-10-20","cited_by_count":75},{"id":"1606.02359","title":"Structure Learning in Graphical Modeling","created":"2016-06-07","cited_by_count":66},{"id":"1905.02325","title":"Sum-of-Squares Polynomial Flow","created":"2019-05-07","cited_by_count":62},{"id":"1907.00389","title":"Coupling techniques for nonlinear ensemble filtering","created":"2019-06-30","cited_by_count":48},{"id":"2407.16212","title":"Optimal experimental design: Formulations and computations","created":"2024-07-23","cited_by_count":45},{"id":"1703.06131","title":"Inference via low-dimensional couplings","created":"2017-03-17","cited_by_count":44},{"id":"2006.06755","title":"Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference","created":"2020-06-11","cited_by_count":42},{"id":"2004.14455","title":"Sparse Cholesky factorization by Kullback-Leibler minimization","created":"2020-04-29","cited_by_count":41},{"id":"2009.10303","title":"On the representation and learning of monotone triangular transport maps","created":"2020-09-22","cited_by_count":41},{"id":"1403.4680","title":"Likelihood-informed dimension reduction for nonlinear inverse problems","created":"2014-03-19","cited_by_count":27},{"id":"1807.03712","title":"Certified dimension reduction in nonlinear Bayesian inverse problems","created":"2018-07-02","cited_by_count":27},{"id":"2309.01043","title":"Distribution learning via neural differential equations: a nonparametric statistical perspective","created":"2023-09-03","cited_by_count":22},{"id":"1606.04062","title":"Causal transport in discrete time and applications","created":"2016-06-13","cited_by_count":21},{"id":"2310.16975","title":"Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference","created":"2023-10-25","cited_by_count":20},{"id":"2207.10231","title":"On minimax density estimation via measure transport","created":"2022-07-20","cited_by_count":18},{"id":"2101.03093","title":"Learning non-Gaussian graphical models via Hessian scores and triangular transport","created":"2021-01-08","cited_by_count":16},{"id":"2112.15595","title":"Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates","created":"2021-12-31","cited_by_count":16},{"id":"2007.06968","title":"Deep composition of tensor-trains using squared inverse Rosenblatt transports","created":"2020-07-14","cited_by_count":12},{"id":"2206.11343","title":"Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport","created":"2022-06-22","cited_by_count":11},{"id":"2108.04211","title":"Scalable Bayesian transport maps for high-dimensional non-Gaussian spatial fields","created":"2021-08-09","cited_by_count":10},{"id":"2411.07154","title":"Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps","created":"2024-11-11","cited_by_count":9},{"id":"1910.11422","title":"Data Driven Conditional Optimal Transport","created":"2019-10-24","cited_by_count":8},{"id":"2210.17435","title":"Ensemble transport smoothing. Part II: Nonlinear updates","created":"2022-10-31","cited_by_count":8},{"id":"2208.03246","title":"Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization","created":"2022-08-05","cited_by_count":7},{"id":"2305.09063","title":"Bounded KRnet and its applications to density estimation and approximation","created":"2023-05-15","cited_by_count":7},{"id":"2401.07971","title":"Tractable Optimal Experimental Design using Transport Maps","created":"2024-01-15","cited_by_count":7},{"id":"2411.12726","title":"LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport","created":"2024-11-19","cited_by_count":7},{"id":"2308.10081","title":"Transporting Higher-Order Quadrature Rules: Quasi-Monte Carlo Points and Sparse Grids for Mixture Distributions","created":"2023-08-19","cited_by_count":6},{"id":"2210.17000","title":"Ensemble transport smoothing. Part I: Unified framework","created":"2022-10-31","cited_by_count":5},{"id":"1501.02320","title":"On model misspecification and KL separation for Gaussian graphical models","created":"2015-01-10","cited_by_count":4},{"id":"2107.04136","title":"Diagonal Nonlinear Transformations Preserve Structure in Covariance and Precision Matrices","created":"2021-07-08","cited_by_count":4},{"id":"2305.18210","title":"Learning Causal Graphs via Monotone Triangular Transport Maps","created":"2023-05-26","cited_by_count":4},{"id":"2411.08390","title":"Expected information gain estimation via density approximations: Sample allocation and dimension reduction","created":"2024-11-13","cited_by_count":4},{"id":"2409.07968","title":"Localized Schr\\\"odinger Bridge Sampler","created":"2024-09-12","cited_by_count":3},{"id":"2502.20086","title":"Subspace accelerated measure transport methods for fast and scalable sequential experimental design, with application to photoacoustic imaging","created":"2025-02-27","cited_by_count":3},{"id":"2308.13940","title":"A transport approach to sequential simulation-based inference","created":"2023-08-26","cited_by_count":2},{"id":"2309.15366","title":"Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences","created":"2023-09-27","cited_by_count":2},{"id":"2310.19000","title":"Distributed Nonlinear Filtering using Triangular Transport Maps","created":"2023-10-29","cited_by_count":2},{"id":"2407.02198","title":"Transport Map Coupling Filter for State-Parameter Estimation","created":"2024-07-02","cited_by_count":2},{"id":"2412.16416","title":"Transport Quasi-Monte Carlo","created":"2024-12-21","cited_by_count":2},{"id":"2503.13899","title":"Learning local neighborhoods of non-Gaussian graphical models: A measure transport approach","created":"2025-03-18","cited_by_count":2},{"id":"2402.17943","title":"Sequential transport maps using SoS density estimation and $\\alpha$-divergences","created":"2024-02-27","cited_by_count":1}],"next_cursor":null}