{"total":21,"rows":[{"id":"2107.04150","title":"MCMC Variational Inference via Uncorrected Hamiltonian Annealing","created":"2021-07-08","cited_by_count":39,"area":null},{"id":"2106.15980","title":"Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence","created":"2021-06-30","cited_by_count":29,"area":null},{"id":"2105.00026","title":"Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder","created":"2021-04-30","cited_by_count":27,"area":null},{"id":"2207.13177","title":"Sliced Wasserstein Variational Inference","created":"2022-07-26","cited_by_count":18,"area":null},{"id":"2102.12956","title":"Stein Variational Gradient Descent: many-particle and long-time asymptotics","created":"2021-02-25","cited_by_count":15,"area":null},{"id":"2112.12194","title":"Surrogate Likelihoods for Variational Annealed Importance Sampling","created":"2021-12-22","cited_by_count":14,"area":null},{"id":"2310.03218","title":"Learning Energy-Based Prior Model with Diffusion-Amortized MCMC","created":"2023-10-05","cited_by_count":13,"area":null},{"id":"2203.09940","title":"Alleviating Adversarial Attacks on Variational Autoencoders with MCMC","created":"2022-03-18","cited_by_count":10,"area":null},{"id":"2401.02080","title":"Energy based diffusion generator for efficient sampling of Boltzmann distributions","created":"2024-01-04","cited_by_count":7,"area":null},{"id":"2206.06295","title":"Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients","created":"2022-06-13","cited_by_count":6,"area":null},{"id":"2310.10379","title":"Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification","created":"2023-10-16","cited_by_count":6,"area":null},{"id":"2410.09355","title":"On Divergence Measures for Training GFlowNets","created":"2024-10-12","cited_by_count":6,"area":null},{"id":"2201.12955","title":"Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework","created":"2022-01-31","cited_by_count":2,"area":null},{"id":"2206.05111","title":"PAVI: Plate-Amortized Variational Inference","created":"2022-06-10","cited_by_count":2,"area":null},{"id":"2309.14612","title":"Reparameterized Variational Rejection Sampling","created":"2023-09-26","cited_by_count":2,"area":null},{"id":"2405.14840","title":"Differentiable Annealed Importance Sampling Minimizes The Symmetrized Kullback-Leibler Divergence Between Initial and Target Distribution","created":"2024-05-23","cited_by_count":2,"area":null},{"id":"2308.16022","title":"PAVI: Plate-Amortized Variational Inference","created":"2023-08-30","cited_by_count":1,"area":null},{"id":"2502.18300","title":"Bayesian Computation in Deep Learning","created":"2025-02-25","cited_by_count":1,"area":null},{"id":"2103.15343","title":"Variational Rejection Particle Filtering","created":"2021-03-29","cited_by_count":0,"area":null},{"id":"2308.13731","title":"Learning variational autoencoders via MCMC speed measures","created":"2023-08-26","cited_by_count":0,"area":null},{"id":"2610.12679","title":"Amortised Inference through One-Step Implicit Sampling","created":"2026-10-08","cited_by_count":0,"area":null}],"next_cursor":null}