{"total":54,"rows":[{"id":"2101.03288","title":"How to Train Your Energy-Based Models","created":"2021-01-09","cited_by_count":274,"area":null},{"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":"2202.11705","title":"COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics","created":"2022-02-23","cited_by_count":161,"area":null},{"id":"2202.01361","title":"Generative Flow Networks for Discrete Probabilistic Modeling","created":"2022-02-03","cited_by_count":114,"area":null},{"id":"2102.04509","title":"Oops I Took A Gradient: Scalable Sampling for Discrete Distributions","created":"2021-02-08","cited_by_count":92,"area":null},{"id":"2107.12628","title":"Energy-Based Open-World Uncertainty Modeling for Confidence Calibration","created":"2021-07-27","cited_by_count":46,"area":null},{"id":"2205.06924","title":"A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model","created":"2022-05-13","cited_by_count":44,"area":null},{"id":"2109.09032","title":"JEM++: Improved Techniques for Training JEM","created":"2021-09-19","cited_by_count":28,"area":null},{"id":"2204.05369","title":"Learning Implicit Priors for Motion Optimization","created":"2022-04-11","cited_by_count":20,"area":null},{"id":"2303.03023","title":"Guiding Energy-based Models via Contrastive Latent Variables","created":"2023-03-06","cited_by_count":18,"area":null},{"id":"2310.18677","title":"Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach","created":"2023-10-28","cited_by_count":18,"area":null},{"id":"2107.08785","title":"On Out-of-distribution Detection with Energy-based Models","created":"2021-07-03","cited_by_count":17,"area":null},{"id":"2209.07959","title":"Towards Bridging the Performance Gaps of Joint Energy-based Models","created":"2022-09-16","cited_by_count":15,"area":null},{"id":"2202.04933","title":"Energy-Based Contrastive Learning of Visual Representations","created":"2022-02-10","cited_by_count":13,"area":null},{"id":"2210.16486","title":"Learning Probabilistic Models from Generator Latent Spaces with Hat EBM","created":"2022-10-29","cited_by_count":11,"area":null},{"id":"2111.00929","title":"Bounds all around: training energy-based models with bidirectional bounds","created":"2021-11-01","cited_by_count":10,"area":null},{"id":"2111.02434","title":"Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling","created":"2021-11-03","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":"2205.09589","title":"Learning Energy Networks with Generalized Fenchel-Young Losses","created":"2022-05-19","cited_by_count":8,"area":null},{"id":"2304.11357","title":"Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training","created":"2023-04-22","cited_by_count":8,"area":null},{"id":"2111.00780","title":"Pseudo-Spherical Contrastive Divergence","created":"2021-11-01","cited_by_count":7,"area":null},{"id":"2312.02469","title":"Learning Energy-based Model via Dual-MCMC Teaching","created":"2023-12-05","cited_by_count":7,"area":null},{"id":"2403.01666","title":"Improving Adversarial Energy-Based Model via Diffusion Process","created":"2024-03-04","cited_by_count":7,"area":null},{"id":"2505.18230","title":"Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models","created":"2025-05-23","cited_by_count":7,"area":null},{"id":"2203.14702","title":"Bi-level Doubly Variational Learning for Energy-based Latent Variable Models","created":"2022-03-24","cited_by_count":6,"area":null},{"id":"2212.13425","title":"GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning","created":"2022-12-27","cited_by_count":6,"area":null},{"id":"2303.04343","title":"M-EBM: Towards Understanding the Manifolds of Energy-Based Models","created":"2023-03-08","cited_by_count":6,"area":null},{"id":"2510.12311","title":"Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics","created":"2025-10-14","cited_by_count":5,"area":null},{"id":"2206.12840","title":"Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One","created":"2022-06-26","cited_by_count":4,"area":null},{"id":"2208.12055","title":"Combating Mode Collapse in GANs via Manifold Entropy Estimation","created":"2022-08-25","cited_by_count":4,"area":null},{"id":"2304.10707","title":"Persistently Trained, Diffusion-assisted Energy-based Models","created":"2023-04-21","cited_by_count":4,"area":null},{"id":"2306.00684","title":"Balanced Training of Energy-Based Models with Adaptive Flow Sampling","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2110.11948","title":"Learning Proposals for Practical Energy-Based Regression","created":"2021-10-22","cited_by_count":3,"area":null},{"id":"2309.15420","title":"The Triad of Failure Modes and a Possible Way Out","created":"2023-09-27","cited_by_count":3,"area":null},{"id":"2105.04538","title":"Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels","created":"2021-05-10","cited_by_count":2,"area":null},{"id":"2106.13798","title":"Conjugate Energy-Based Models","created":"2021-06-25","cited_by_count":2,"area":null},{"id":"2302.00695","title":"Versatile Energy-Based Probabilistic Models for High Energy Physics","created":"2023-02-01","cited_by_count":2,"area":null},{"id":"2309.10711","title":"Latent Space Energy-based Model for Fine-grained Open Set Recognition","created":"2023-09-19","cited_by_count":2,"area":null},{"id":"2401.00873","title":"Unifying Self-Supervised Clustering and Energy-Based Models","created":"2023-12-30","cited_by_count":2,"area":null},{"id":"2504.16262","title":"Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching","created":"2025-04-22","cited_by_count":2,"area":null},{"id":"2111.01035","title":"A Unified View of cGANs with and without Classifiers","created":"2021-11-01","cited_by_count":1,"area":null},{"id":"2210.06302","title":"Maximum entropy exploration in contextual bandits with neural networks and energy based models","created":"2022-10-12","cited_by_count":1,"area":null},{"id":"2302.12002","title":"Master's Thesis: Out-of-distribution Detection with Energy-based Models","created":"2023-01-28","cited_by_count":1,"area":null},{"id":"2303.03887","title":"How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model","created":"2023-03-05","cited_by_count":1,"area":null},{"id":"2306.14448","title":"Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation","created":"2023-06-26","cited_by_count":1,"area":null},{"id":"2410.03505","title":"Classification-Denoising Networks","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2510.13872","title":"Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation","created":"2025-10-13","cited_by_count":1,"area":null},{"id":"2510.15447","title":"Particle Dynamics for Latent-Variable Energy-Based Models","created":"2025-10-17","cited_by_count":1,"area":null},{"id":"2205.12243","title":"EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling","created":"2022-05-24","cited_by_count":0,"area":null},{"id":"2303.04187","title":"Stabilized training of joint energy-based models and their practical applications","created":"2023-03-07","cited_by_count":0,"area":null},{"id":"2407.15238","title":"Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling","created":"2024-07-21","cited_by_count":0,"area":null},{"id":"2409.01890","title":"A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks","created":"2024-09-03","cited_by_count":0,"area":null},{"id":"2503.07021","title":"Learning Energy-Based Models by Self-normalising the Likelihood","created":"2025-03-10","cited_by_count":0,"area":null},{"id":"2506.04609","title":"Exploring bidirectional bounds for minimax-training of Energy-based models","created":"2025-06-05","cited_by_count":0,"area":null}],"next_cursor":null}