{"list":"other","total":55,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"1306.0895","title":"Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances","created":"2013-06-04","cited_by_count":2316},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1601.00670","title":"Variational Inference: A Review for Statisticians","created":"2016-01-04","cited_by_count":1472},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"2107.00630","title":"Variational Diffusion Models","created":"2021-07-01","cited_by_count":1153},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"2303.08797","title":"Stochastic Interpolants: A Unifying Framework for Flows and Diffusions","created":"2023-03-15","cited_by_count":939},{"id":"1705.07057","title":"Masked Autoregressive Flow for Density Estimation","created":"2017-05-19","cited_by_count":684},{"id":"2106.01357","title":"Diffusion Schr\\\"odinger Bridge with Applications to Score-Based Generative Modeling","created":"2021-06-01","cited_by_count":602},{"id":"1509.00519","title":"Importance Weighted Autoencoders","created":"2015-09-01","cited_by_count":456},{"id":"1905.07088","title":"Sliced Score Matching: A Scalable Approach to Density and Score Estimation","created":"2019-05-17","cited_by_count":384},{"id":"1308.0215","title":"A survey of the Schr\\\"odinger problem and some of its connections with optimal transport","created":"2013-08-01","cited_by_count":292},{"id":"2111.09266","title":"GFlowNet Foundations","created":"2021-11-17","cited_by_count":290},{"id":"2110.11291","title":"Likelihood Training of Schr\\\"odinger Bridge using Forward-Backward SDEs Theory","created":"2021-10-21","cited_by_count":219},{"id":"2106.02808","title":"A Variational Perspective on Diffusion-Based Generative Models and Score Matching","created":"2021-06-05","cited_by_count":183},{"id":"2111.15141","title":"Path Integral Sampler: a stochastic control approach for sampling","created":"2021-11-30","cited_by_count":181},{"id":"1905.09883","title":"Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit","created":"2019-05-23","cited_by_count":176},{"id":"2002.06707","title":"Stochastic Normalizing Flows","created":"2020-02-16","cited_by_count":157},{"id":"2302.13834","title":"Denoising Diffusion Samplers","created":"2023-02-27","cited_by_count":149},{"id":"2211.01364","title":"An optimal control perspective on diffusion-based generative modeling","created":"2022-11-02","cited_by_count":147},{"id":"2006.00104","title":"OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport","created":"2020-05-29","cited_by_count":143},{"id":"2106.02081","title":"Solving Schr\\\"odinger Bridges via Maximum Likelihood","created":"2021-06-03","cited_by_count":141},{"id":"2208.01893","title":"Flow Annealed Importance Sampling Bootstrap","created":"2022-08-03","cited_by_count":127},{"id":"1602.02311","title":"R\\'enyi Divergence Variational Inference","created":"2016-02-06","cited_by_count":104},{"id":"2208.14699","title":"Let us Build Bridges: Understanding and Extending Diffusion Generative Models","created":"2022-08-31","cited_by_count":103},{"id":"1412.4430","title":"On the relation between optimal transport and Schr\\\"odinger bridges: A stochastic control viewpoint","created":"2014-12-15","cited_by_count":99},{"id":"1703.09194","title":"Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference","created":"2017-03-27","cited_by_count":98},{"id":"2210.06662","title":"Action Matching: Learning Stochastic Dynamics from Samples","created":"2022-10-13","cited_by_count":98},{"id":"1903.01608","title":"Theoretical guarantees for sampling and inference in generative models with latent diffusions","created":"2019-03-05","cited_by_count":97},{"id":"2102.07501","title":"Annealed Flow Transport Monte Carlo","created":"2021-02-15","cited_by_count":90},{"id":"2005.05409","title":"Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space","created":"2020-05-11","cited_by_count":82},{"id":"2304.00917","title":"Diffusion Bridge Mixture Transports, Schr\\\"odinger Bridge Problems and Generative Modeling","created":"2023-04-03","cited_by_count":80},{"id":"2110.07579","title":"Diffusion Normalizing Flow","created":"2021-10-14","cited_by_count":77},{"id":"2010.10436","title":"VarGrad: A Low-Variance Gradient Estimator for Variational Inference","created":"2020-10-20","cited_by_count":75},{"id":"2201.13117","title":"Continual Repeated Annealed Flow Transport Monte Carlo","created":"2022-01-31","cited_by_count":66},{"id":"2111.10510","title":"Bayesian Learning via Neural Schr\\\"odinger-F\\\"ollmer Flows","created":"2021-11-20","cited_by_count":56},{"id":"2208.07698","title":"Score-Based Diffusion meets Annealed Importance Sampling","created":"2022-08-16","cited_by_count":51},{"id":"2211.01156","title":"Entropic Neural Optimal Transport via Diffusion Processes","created":"2022-11-02","cited_by_count":50},{"id":"2107.04150","title":"MCMC Variational Inference via Uncorrected Hamiltonian Annealing","created":"2021-07-08","cited_by_count":39},{"id":"2102.06559","title":"Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations","created":"2021-02-12","cited_by_count":38},{"id":"1511.03243","title":"Black-box $\\alpha$-divergence Minimization","created":"2015-11-10","cited_by_count":37},{"id":"2106.15921","title":"Monte Carlo Variational Auto-Encoders","created":"2021-06-30","cited_by_count":36},{"id":"2107.10211","title":"Differentiable Annealed Importance Sampling and the Perils of Gradient Noise","created":"2021-07-21","cited_by_count":36},{"id":"2304.13534","title":"A mean-field games laboratory for generative modeling","created":"2023-04-26","cited_by_count":28},{"id":"2208.07743","title":"Langevin Diffusion Variational Inference","created":"2022-08-16","cited_by_count":26},{"id":"2112.03749","title":"Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems","created":"2021-12-07","cited_by_count":21},{"id":"2209.01170","title":"First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data","created":"2022-09-02","cited_by_count":18},{"id":"2210.06226","title":"Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics","created":"2022-10-12","cited_by_count":15},{"id":"1912.13170","title":"Schr\\\"odinger Bridge Samplers","created":"2019-12-31","cited_by_count":14},{"id":"2306.15283","title":"Adaptive Annealed Importance Sampling with Constant Rate Progress","created":"2023-06-27","cited_by_count":7},{"id":"1802.01271","title":"Stochastic control and non-equilibrium thermodynamics: fundamental limits","created":"2018-02-05","cited_by_count":4}],"next_cursor":null}