{"total":64,"rows":[{"id":"2604.28190","title":"Representation Fr\\'echet Loss for Visual Generation","created":"2026-04-30","cited_by_count":37,"area":null},{"id":"2603.12366","title":"Sinkhorn-Drifting Generative Models","created":"2026-03-12","cited_by_count":24,"area":null},{"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":"2603.10592","title":"Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences","created":"2026-03-11","cited_by_count":17,"area":null},{"id":"2604.06333","title":"Drifting Fields are not Conservative","created":"2026-04-07","cited_by_count":13,"area":null},{"id":"2605.05118","title":"On the Wasserstein Gradient Flow Interpretation of Drifting Models","created":"2026-05-06","cited_by_count":11,"area":null},{"id":"2605.10727","title":"Kernel-Gradient Drifting Models","created":"2026-05-11","cited_by_count":7,"area":null},{"id":"2605.22795","title":"Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models","created":"2026-05-21","cited_by_count":4,"area":null},{"id":"2607.18198","title":"Three-Body Scattering for Generative Modeling","created":"2026-07-20","cited_by_count":4,"area":null},{"id":"2604.18194","title":"Friction-Augmented Drifting Models for Resource-Efficient Domain Translation","created":"2026-04-20","cited_by_count":3,"area":null},{"id":"2604.24199","title":"Speech Enhancement Based on Drifting Models","created":"2026-04-27","cited_by_count":3,"area":null},{"id":"2605.07727","title":"Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow","created":"2026-05-08","cited_by_count":3,"area":null},{"id":"2605.12183","title":"DriftXpress: Faster Drifting Models via Projected RKHS Fields","created":"2026-05-12","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":"2606.03199","title":"Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching","created":"2026-06-02","cited_by_count":3,"area":null},{"id":"2606.15553","title":"Distilling Drifting Transformers with Representation Autoencoders","created":"2026-06-14","cited_by_count":3,"area":null},{"id":"2606.19935","title":"PhysDrift: Bridging the Embodiment Gap in Humanoid Co-Speech Motion Generation","created":"2026-06-18","cited_by_count":3,"area":null},{"id":"2603.11984","title":"Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation","created":"2026-03-12","cited_by_count":2,"area":null},{"id":"2603.14186","title":"Setting-Matched and Semantics-Scaled Benchmarking of One-Step Generative Models Against Multistep Diffusion and Flow Models","created":"2026-03-15","cited_by_count":2,"area":null},{"id":"2604.19736","title":"Generative Drifting for Conditional Medical Image Generation","created":"2026-04-21","cited_by_count":2,"area":null},{"id":"2605.14142","title":"To discretize continually: Mean shift interacting particle systems for Bayesian inference","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2605.28075","title":"Measure-to-measure Regression with Transformers","created":"2026-05-27","cited_by_count":2,"area":null},{"id":"2605.31369","title":"A Unifying View of Variational Generative Wasserstein Flows","created":"2026-05-29","cited_by_count":2,"area":null},{"id":"2606.22527","title":"Trajectory Forcing: Structure-First Generation with Controllable Semantic Trajectories","created":"2026-06-21","cited_by_count":2,"area":null},{"id":"2607.00149","title":"Uniform-in-time Propagation-of-Chaos for Stein Variational Gradient Descent","created":"2026-06-30","cited_by_count":2,"area":null},{"id":"2607.26860","title":"Amortized Moment Matching for Visual Generation","created":"2026-07-29","cited_by_count":2,"area":null},{"id":"2603.17579","title":"One-Step Sampler for Boltzmann Distributions via Drifting","created":"2026-03-18","cited_by_count":1,"area":null},{"id":"2604.04528","title":"Receding-Horizon Control via Drifting Models","created":"2026-04-06","cited_by_count":1,"area":null},{"id":"2604.21489","title":"MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting","created":"2026-04-23","cited_by_count":1,"area":null},{"id":"2604.27239","title":"Analytical Correction for Subsampling Bias in Drifting Models","created":"2026-04-29","cited_by_count":1,"area":null},{"id":"2605.04060","title":"Lookahead Drifting Model","created":"2026-04-10","cited_by_count":1,"area":null},{"id":"2605.15592","title":"Efficient Image Synthesis with Sphere Latent Encoder","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2606.00514","title":"Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation","created":"2026-05-30","cited_by_count":1,"area":null},{"id":"2608.07924","title":"Second Order Drifting Models","created":"2026-08-08","cited_by_count":1,"area":null},{"id":"2609.35763","title":"Unifying Distributional Training for One-Step Visual Generation","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2603.28049","title":"Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting","created":"2026-03-30","cited_by_count":0,"area":null},{"id":"2604.24196","title":"Identifiability and Stability of Generative Drifting in the Companion-Elliptic Kernel Family","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2605.06140","title":"SymDrift: One-Shot Generative Modeling under Symmetries","created":"2026-05-07","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":"2605.19060","title":"LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.13366","title":"Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2606.27767","title":"Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2606.27824","title":"Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2607.02428","title":"Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2607.04740","title":"DriftST: One-Step Generative Inference of Spatial Transcriptomics from H\\&E Histology","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.01547","title":"Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2609.12252","title":"DriftSE: Speech Enhancement with Generative Drifting","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.32298","title":"Refresh or Realize? Compute Allocation in Drifting Models","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.32708","title":"One-Step Generative Modeling via Unbalanced Optimal Transport","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.33217","title":"RepFlow: Reciprocal Supervision Improves Generation and Representation in Flow Models","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.35491","title":"From Scores to Samples: Elastic Forcing for Autoregressive Video Generation","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.35512","title":"Improving Generative Model Self-Training with Geometrically Modified Outputs","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.39271","title":"RW-Flow: One-Step Generation on Compact Manifolds via Wasserstein Gradient Flows","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00518","title":"One-Step Generative Modeling via Training Dynamics Action","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.01355","title":"Discrete Wasserstein Flows for One-Step Generative Modeling","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.02208","title":"Sphere Encoder 2","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.03626","title":"Depth as Time in One-Step Generative Models","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.04454","title":"One-Step Generation via Riemannian Wasserstein Gradient Flows","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.04703","title":"Learning Discriminative Geometry for Drifting Models","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.05900","title":"End-to-End Autonomous Recursive Arborescence Deformable Flow and Non-Linear Hemodynamics for Patient-Specific Coronary Centerline Extraction","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.10989","title":"Multi-Bandwidth Distribution Matching Distillation: On the Equivalence of Distribution Matching Distillation and Drifting Models","created":"2026-10-07","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":"2610.17466","title":"Efficient Fr\\'echet Distributional Decoder Alignment for Latent Generative Models","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.18552","title":"Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}