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[2501.09876] Geometry-Preserving Encoder/Decoder in Latent Generative Models
Abstract page for arXiv paper 2501.09876: Geometry-Preserving Encoder/Decoder in Latent Generative Models
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[2506.22304] Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
Abstract page for arXiv paper 2506.22304: Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
[2605.31296] mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
Abstract page for arXiv paper 2605.31296: mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
[2605.30453] Generative Models and Statistical Validation
Abstract page for arXiv paper 2605.30453: Generative Models and Statistical Validation
[2605.30153] Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions
Abstract page for arXiv paper 2605.30153: Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions
[2605.28867] PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
Abstract page for arXiv paper 2605.28867: PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
[2510.11499] Offline Reinforcement Learning with Generative Trajectory Policies
Abstract page for arXiv paper 2510.11499: Offline Reinforcement Learning with Generative Trajectory Policies
[2605.27478] Triangular-Reference Schrödinger Bridges for Time Series Generation
Abstract page for arXiv paper 2605.27478: Triangular-Reference Schrödinger Bridges for Time Series Generation
[2605.27523] Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions
Abstract page for arXiv paper 2605.27523: Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions
[2605.27564] The Future of Facts: Tracing the Factual Generation-Verification Gap
Abstract page for arXiv paper 2605.27564: The Future of Facts: Tracing the Factual Generation-Verification Gap
[2605.28137] No Safe Dose: How Training Data Drives Unsafe Image Generation
Abstract page for arXiv paper 2605.28137: No Safe Dose: How Training Data Drives Unsafe Image Generation