generative modeling

Flow Matching for Generative Modeling ICLR 2023 Flow Matching for Scalable Simulation-Based Inference NeurIPS 2023 Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow ICLR 2023 Formulating Discrete Probability Flow Through Optimal Transport NeurIPS 2023 Functional Renyi Differential Privacy for Generative Modeling NeurIPS 2023 Generative Category-level Object Pose Estimation via Diffusion Models NeurIPS 2023 Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport NeurIPS 2023 GINA-3D: Learning To Generate Implicit Neural Assets in the Wild CVPR 2023 Hierarchical Sliced Wasserstein Distance ICLR 2023 High Fidelity Image Counterfactuals with Probabilistic Causal Models ICML 2023 HOLODIFFUSION: Training a 3D Diffusion Model Using 2D Images CVPR 2023 HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion ICCV 2023 Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling ICML 2023 Learning with Stochastic Orders ICLR 2023 Mirror Diffusion Models for Constrained and Watermarked Generation NeurIPS 2023 Moment Matching Denoising Gibbs Sampling NeurIPS 2023 OBJECT 3DIT: Language-guided 3D-aware Image Editing NeurIPS 2023 OCTET: Object-Aware Counterfactual Explanations CVPR 2023 On Calibrating Diffusion Probabilistic Models NeurIPS 2023 Optimizing DDPM Sampling with Shortcut Fine-Tuning ICML 2023 Polynomial Implicit Neural Representations for Large Diverse Datasets CVPR 2023 Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation ICML 2023 Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows ICML 2023 Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers ICML 2023 Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling NeurIPS 2023