{"total":49,"rows":[{"id":"2210.00580","title":"GFlowNets and variational inference","created":"2022-10-02","cited_by_count":106,"area":null},{"id":"2307.01198","title":"Improved sampling via learned diffusions","created":"2023-07-03","cited_by_count":104,"area":null},{"id":"2111.10510","title":"Bayesian Learning via Neural Schr\\\"odinger-F\\\"ollmer Flows","created":"2021-11-20","cited_by_count":56,"area":null},{"id":"2406.07423","title":"Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling","created":"2024-06-11","cited_by_count":56,"area":null},{"id":"2403.07041","title":"Ant Colony Sampling with GFlowNets for Combinatorial Optimization","created":"2024-03-11","cited_by_count":53,"area":null},{"id":"2510.01135","title":"Prompt Curriculum Learning for Efficient LLM Post-Training","created":"2025-10-01","cited_by_count":45,"area":null},{"id":"2205.04766","title":"Explainable Deep Learning Methods in Medical Image Classification: A Survey","created":"2022-05-10","cited_by_count":32,"area":null},{"id":"2503.01006","title":"Underdamped Diffusion Bridges with Applications to Sampling","created":"2025-03-02","cited_by_count":29,"area":null},{"id":"2502.06685","title":"No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers","created":"2025-02-10","cited_by_count":28,"area":null},{"id":"2202.09497","title":"Gradient Estimation with Discrete Stein Operators","created":"2022-02-19","cited_by_count":23,"area":null},{"id":"2207.08219","title":"Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows","created":"2022-07-17","cited_by_count":23,"area":null},{"id":"2508.12511","title":"Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference","created":"2025-08-17","cited_by_count":23,"area":null},{"id":"2307.01050","title":"Transport meets Variational Inference: Controlled Monte Carlo Diffusions","created":"2023-07-03","cited_by_count":21,"area":null},{"id":"2311.12569","title":"Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick","created":"2023-11-21","cited_by_count":14,"area":null},{"id":"2307.03675","title":"GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies","created":"2023-07-07","cited_by_count":10,"area":null},{"id":"2405.19961","title":"Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers","created":"2024-05-30","cited_by_count":10,"area":null},{"id":"2106.08056","title":"Coupled Gradient Estimators for Discrete Latent Variables","created":"2021-06-15","cited_by_count":9,"area":null},{"id":"2111.05300","title":"Double Control Variates for Gradient Estimation in Discrete Latent Variable Models","created":"2021-11-09","cited_by_count":9,"area":null},{"id":"2301.05475","title":"Designing losses for data-free training of normalizing flows on Boltzmann distributions","created":"2023-01-13","cited_by_count":9,"area":null},{"id":"2510.18460","title":"Learning Boltzmann Generators via Constrained Mass Transport","created":"2025-10-21","cited_by_count":9,"area":null},{"id":"2310.20090","title":"Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows","created":"2023-10-31","cited_by_count":8,"area":null},{"id":"2110.15072","title":"Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces","created":"2021-10-28","cited_by_count":7,"area":null},{"id":"2506.10982","title":"Rethinking Losses for Diffusion Bridge Samplers","created":"2025-06-12","cited_by_count":7,"area":null},{"id":"2312.02438","title":"Adaptive Instrument Design for Indirect Experiments","created":"2023-12-05","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":"2510.11711","title":"Reinforced sequential Monte Carlo for amortised sampling","created":"2025-10-13","cited_by_count":6,"area":null},{"id":"2412.08824","title":"Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI","created":"2024-12-11","cited_by_count":5,"area":null},{"id":"2110.14002","title":"CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator","created":"2021-10-26","cited_by_count":4,"area":null},{"id":"2411.05899","title":"Streaming Bayes GFlowNets","created":"2024-11-08","cited_by_count":4,"area":null},{"id":"2503.21673","title":"A friendly introduction to triangular transport","created":"2025-03-27","cited_by_count":4,"area":null},{"id":"2605.15417","title":"$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data","created":"2026-05-14","cited_by_count":4,"area":null},{"id":"2406.03288","title":"Embarrassingly Parallel GFlowNets","created":"2024-06-05","cited_by_count":3,"area":null},{"id":"2510.09596","title":"BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards","created":"2025-10-10","cited_by_count":3,"area":null},{"id":"2602.03729","title":"Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization","created":"2026-02-03","cited_by_count":3,"area":null},{"id":"2605.26552","title":"Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference","created":"2026-05-26","cited_by_count":3,"area":null},{"id":"2604.17140","title":"Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models","created":"2026-04-18","cited_by_count":2,"area":null},{"id":"2307.02454","title":"Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness","created":"2023-07-05","cited_by_count":1,"area":null},{"id":"2509.23849","title":"CE-FAM: Concept-Based Explanation via Fusion of Activation Maps","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2609.02948","title":"FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions","created":"2026-09-01","cited_by_count":1,"area":null},{"id":"2211.00642","title":"Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks","created":"2022-10-31","cited_by_count":0,"area":null},{"id":"2402.10339","title":"What to Do When Your Discrete Optimization Is the Size of a Neural Network?","created":"2024-02-15","cited_by_count":0,"area":null},{"id":"2507.11759","title":"Torsional-GFN: a conditional conformation generator for small molecules","created":"2025-07-15","cited_by_count":0,"area":null},{"id":"2602.03358","title":"GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.22884","title":"Unsupervised Continual Learning for Amortized Bayesian Inference","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2605.17808","title":"A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2608.08644","title":"Path-dependent Discrete Amortized Inference","created":"2026-08-09","cited_by_count":0,"area":null},{"id":"2609.35514","title":"One Proposal for Every Margin: Zero-Shot Amortized Sequential Importance Sampling for Binary Matrices","created":"2026-09-28","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},{"id":"2610.19781","title":"SNACK: A Sequential Notation Framework for Probabilistic Graph Generation","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}