{"total":274,"rows":[{"id":"2108.01073","title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations","created":"2021-08-02","cited_by_count":1937,"area":null},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114,"area":"diffusion"},{"id":"2111.08005","title":"Solving Inverse Problems in Medical Imaging with Score-Based Generative Models","created":"2021-11-15","cited_by_count":521,"area":"diffusion"},{"id":"2109.00137","title":"Implicit Behavioral Cloning","created":"2021-09-01","cited_by_count":452,"area":"offlinerl"},{"id":"2208.11970","title":"Understanding Diffusion Models: A Unified Perspective","created":"2022-08-25","cited_by_count":340,"area":"diffusion"},{"id":"2101.12072","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","created":"2021-01-28","cited_by_count":337,"area":"diffusion"},{"id":"2110.05243","title":"Score-based diffusion models for accelerated MRI","created":"2021-10-08","cited_by_count":311,"area":"diffusion"},{"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,"area":null},{"id":"2110.07728","title":"Pre-training Molecular Graph Representation with 3D Geometry","created":"2021-10-07","cited_by_count":248,"area":null},{"id":"2209.02646","title":"A Survey on Generative Diffusion Model","created":"2022-09-06","cited_by_count":240,"area":"diffusion"},{"id":"2207.14626","title":"Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models","created":"2022-07-29","cited_by_count":215,"area":"diffusion"},{"id":"2106.02808","title":"A Variational Perspective on Diffusion-Based Generative Models and Score Matching","created":"2021-06-05","cited_by_count":183,"area":"diffusion"},{"id":"2407.18219","title":"Recursive Introspection: Teaching Language Model Agents How to Self-Improve","created":"2024-07-25","cited_by_count":165,"area":"lr"},{"id":"2309.02473","title":"A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges","created":"2023-09-05","cited_by_count":148,"area":"offlinerl"},{"id":"2106.03802","title":"Learning to Efficiently Sample from Diffusion Probabilistic Models","created":"2021-06-07","cited_by_count":129,"area":"diffusion"},{"id":"2209.03855","title":"SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion","created":"2022-09-08","cited_by_count":127,"area":"diffusion"},{"id":"2202.01361","title":"Generative Flow Networks for Discrete Probabilistic Modeling","created":"2022-02-03","cited_by_count":114,"area":null},{"id":"2102.00546","title":"GraphEBM: Molecular Graph Generation with Energy-Based Models","created":"2021-01-31","cited_by_count":85,"area":null},{"id":"2303.11717","title":"A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?","created":"2023-03-21","cited_by_count":84,"area":null},{"id":"2410.10088","title":"The Ingredients for Robotic Diffusion Transformers","created":"2024-10-14","cited_by_count":73,"area":"diffusion"},{"id":"2302.00600","title":"Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics","created":"2023-02-01","cited_by_count":72,"area":"diffusion"},{"id":"2304.09355","title":"To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review","created":"2023-04-19","cited_by_count":72,"area":null},{"id":"2502.00361","title":"Efficient Online Reinforcement Learning for Diffusion Policy","created":"2025-02-01","cited_by_count":71,"area":"diffusion"},{"id":"2206.13602","title":"Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching","created":"2022-06-27","cited_by_count":68,"area":null},{"id":"2203.14500","title":"MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design","created":"2022-03-28","cited_by_count":66,"area":null},{"id":"2110.10873","title":"Controllable and Compositional Generation with Latent-Space Energy-Based Models","created":"2021-10-21","cited_by_count":64,"area":null},{"id":"2111.04726","title":"Estimating High Order Gradients of the Data Distribution by Denoising","created":"2021-11-08","cited_by_count":64,"area":null},{"id":"2302.04611","title":"A Text-guided Protein Design Framework","created":"2023-02-09","cited_by_count":64,"area":null},{"id":"2402.07747","title":"Optimal score estimation via empirical Bayes smoothing","created":"2024-02-12","cited_by_count":61,"area":null},{"id":"2306.04542","title":"On the Design Fundamentals of Diffusion Models: A Survey","created":"2023-06-07","cited_by_count":60,"area":"diffusion"},{"id":"2312.10527","title":"CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems","created":"2023-12-16","cited_by_count":56,"area":"diffusion"},{"id":"2110.00351","title":"Smooth Normalizing Flows","created":"2021-10-01","cited_by_count":53,"area":null},{"id":"2111.07243","title":"Simulating Diffusion Bridges with Score Matching","created":"2021-11-14","cited_by_count":52,"area":null},{"id":"2303.16535","title":"Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning","created":"2023-03-29","cited_by_count":48,"area":null},{"id":"2206.04642","title":"Probability flow solution of the Fokker-Planck equation","created":"2022-06-09","cited_by_count":47,"area":null},{"id":"2502.13129","title":"Is Noise Conditioning Necessary for Denoising Generative Models?","created":"2025-02-18","cited_by_count":44,"area":"diffusion"},{"id":"2106.10121","title":"ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models","created":"2021-06-18","cited_by_count":43,"area":null},{"id":"2302.13335","title":"Diffusion Model-Augmented Behavioral Cloning","created":"2023-02-26","cited_by_count":38,"area":"diffusion"},{"id":"2203.11167","title":"Flow-matching -- efficient coarse-graining of molecular dynamics without forces","created":"2022-03-21","cited_by_count":36,"area":"diffusion"},{"id":"2408.04380","title":"Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations","created":"2024-08-08","cited_by_count":35,"area":"offlinerl"},{"id":"2409.06219","title":"Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning","created":"2024-09-10","cited_by_count":35,"area":null},{"id":"2305.18407","title":"A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining","created":"2023-05-28","cited_by_count":32,"area":null},{"id":"2307.04751","title":"Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement","created":"2023-07-10","cited_by_count":29,"area":null},{"id":"2304.13534","title":"A mean-field games laboratory for generative modeling","created":"2023-04-26","cited_by_count":28,"area":null},{"id":"2204.03236","title":"Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum","created":"2022-04-07","cited_by_count":27,"area":null},{"id":"2504.10612","title":"Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling","created":"2025-04-14","cited_by_count":27,"area":"diffusion"},{"id":"2105.05735","title":"Autoencoding Under Normalization Constraints","created":"2021-05-12","cited_by_count":26,"area":null},{"id":"2304.06094","title":"Energy-guided Entropic Neural Optimal Transport","created":"2023-04-12","cited_by_count":26,"area":null},{"id":"2605.05115","title":"Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior","created":"2026-05-06","cited_by_count":26,"area":null},{"id":"2311.14402","title":"TEA: Test-time Energy Adaptation","created":"2023-11-24","cited_by_count":25,"area":null},{"id":"2403.01189","title":"Training Unbiased Diffusion Models From Biased Dataset","created":"2024-03-02","cited_by_count":25,"area":"diffusion"},{"id":"2405.16418","title":"Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective","created":"2024-05-26","cited_by_count":24,"area":"diffusion"},{"id":"2412.08021","title":"Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning","created":"2024-12-11","cited_by_count":21,"area":"deeprl"},{"id":"2204.05369","title":"Learning Implicit Priors for Motion Optimization","created":"2022-04-11","cited_by_count":20,"area":null},{"id":"2502.12786","title":"Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo","created":"2025-02-18","cited_by_count":20,"area":"diffusion"},{"id":"2603.09936","title":"Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective","created":"2026-03-10","cited_by_count":20,"area":null},{"id":"2211.13837","title":"End-to-End Stochastic Optimization with Energy-Based Model","created":"2022-11-25","cited_by_count":19,"area":null},{"id":"2410.14250","title":"Vision-Language Navigation with Energy-Based Policy","created":"2024-10-18","cited_by_count":19,"area":null},{"id":"2310.01174","title":"Light Schr\\\"odinger Bridge","created":"2023-10-02","cited_by_count":18,"area":null},{"id":"2312.09597","title":"Deep Generative Models for Detector Signature Simulation: A Taxonomic Review","created":"2023-12-15","cited_by_count":18,"area":null},{"id":"2506.05310","title":"Learning normalized image densities via dual score matching","created":"2025-06-05","cited_by_count":18,"area":null},{"id":"2401.12689","title":"Energy-based Automated Model Evaluation","created":"2024-01-23","cited_by_count":17,"area":null},{"id":"2407.04493","title":"PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation","created":"2024-07-05","cited_by_count":16,"area":"diffusion"},{"id":"2301.05579","title":"A survey and taxonomy of loss functions in machine learning","created":"2023-01-13","cited_by_count":15,"area":null},{"id":"2302.01667","title":"Mind the Gap: Offline Policy Optimization for Imperfect Rewards","created":"2023-02-03","cited_by_count":15,"area":"offlinerl"},{"id":"2305.19414","title":"Efficient Training of Energy-Based Models Using Jarzynski Equality","created":"2023-05-30","cited_by_count":15,"area":null},{"id":"2405.03251","title":"Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond","created":"2024-05-06","cited_by_count":15,"area":"diffusion"},{"id":"2406.16121","title":"Diffusion Spectral Representation for Reinforcement Learning","created":"2024-06-23","cited_by_count":15,"area":"diffusion"},{"id":"2302.11215","title":"Energy-Based Test Sample Adaptation for Domain Generalization","created":"2023-02-22","cited_by_count":14,"area":null},{"id":"2512.05116","title":"Value Gradient Guidance for Flow Matching Alignment","created":"2025-12-04","cited_by_count":14,"area":"diffusion"},{"id":"2202.04933","title":"Energy-Based Contrastive Learning of Visual Representations","created":"2022-02-10","cited_by_count":13,"area":null},{"id":"2209.07396","title":"Towards Healing the Blindness of Score Matching","created":"2022-09-15","cited_by_count":13,"area":null},{"id":"2209.14593","title":"Denoising MCMC for Accelerating Diffusion-Based Generative Models","created":"2022-09-29","cited_by_count":13,"area":"diffusion"},{"id":"2302.01075","title":"MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows","created":"2023-02-02","cited_by_count":13,"area":null},{"id":"2408.02226","title":"ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation","created":"2024-08-05","cited_by_count":13,"area":"diffusion"},{"id":"2408.04093","title":"Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters","created":"2024-08-07","cited_by_count":13,"area":null},{"id":"2503.05079","title":"On a Connection Between Imitation Learning and RLHF","created":"2025-03-07","cited_by_count":13,"area":"alignment"},{"id":"2203.04695","title":"Structured Multi-task Learning for Molecular Property Prediction","created":"2022-02-22","cited_by_count":12,"area":null},{"id":"2207.06812","title":"Comparing the latent space of generative models","created":"2022-07-14","cited_by_count":12,"area":null},{"id":"2310.01105","title":"Energy-Guided Continuous Entropic Barycenter Estimation for General Costs","created":"2023-10-02","cited_by_count":12,"area":null},{"id":"2407.09024","title":"Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control","created":"2024-07-12","cited_by_count":12,"area":"diffusion"},{"id":"2409.10353","title":"Taming Diffusion Models for Image Restoration: A Review","created":"2024-09-16","cited_by_count":12,"area":"diffusion"},{"id":"2308.14328","title":"Reinforcement Learning for Generative AI: A Survey","created":"2023-08-28","cited_by_count":11,"area":null},{"id":"2405.17955","title":"Efficient Prior Calibration From Indirect Data","created":"2024-05-28","cited_by_count":11,"area":null},{"id":"2410.20688","title":"Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design","created":"2024-10-28","cited_by_count":11,"area":"diffusion"},{"id":"2502.04575","title":"Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond","created":"2025-02-07","cited_by_count":11,"area":null},{"id":"2306.04689","title":"Multiscale Flow for Robust and Optimal Cosmological Analysis","created":"2023-06-07","cited_by_count":10,"area":null},{"id":"2309.05803","title":"Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models","created":"2023-09-11","cited_by_count":10,"area":"diffusion"},{"id":"2111.02434","title":"Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling","created":"2021-11-03","cited_by_count":9,"area":null},{"id":"2304.12906","title":"The Score-Difference Flow for Implicit Generative Modeling","created":"2023-04-25","cited_by_count":9,"area":"diffusion"},{"id":"2305.16966","title":"Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection","created":"2023-05-26","cited_by_count":9,"area":null},{"id":"2307.01668","title":"Training Energy-Based Models with Diffusion Contrastive Divergences","created":"2023-07-04","cited_by_count":9,"area":null},{"id":"2307.06431","title":"Energy Discrepancies: A Score-Independent Loss for Energy-Based Models","created":"2023-07-12","cited_by_count":9,"area":null},{"id":"2312.01811","title":"Energy-based Potential Games for Joint Motion Forecasting and Control","created":"2023-12-04","cited_by_count":9,"area":null},{"id":"2405.15376","title":"Fast training and sampling of Restricted Boltzmann Machines","created":"2024-05-24","cited_by_count":9,"area":null},{"id":"2412.13862","title":"Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model","created":"2024-12-18","cited_by_count":9,"area":"alignment"},{"id":"2505.19601","title":"Preference Optimization by Estimating the Ratio of the Data Distribution","created":"2025-05-26","cited_by_count":9,"area":"alignment"},{"id":"2104.07531","title":"On Energy-Based Models with Overparametrized Shallow Neural Networks","created":"2021-04-15","cited_by_count":8,"area":null},{"id":"2107.05134","title":"Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks","created":"2021-07-11","cited_by_count":8,"area":null},{"id":"2205.09589","title":"Learning Energy Networks with Generalized Fenchel-Young Losses","created":"2022-05-19","cited_by_count":8,"area":null}],"next_cursor":"100"}