A new variational inference method uses neural networks to tilt Lévy measures, enabling scalable posterior inference for jump processes while preserving their discontinuous structure.
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arXiv:1905.09883 , year=
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A two-stage symbolic regression plus generative model framework recovers governing interaction terms and forcing in stochastic triad models while accurately predicting statistical moments up to order five.
A neural path estimation approach learns the filtering posterior path measure for stochastic dynamical systems from noisy partial observations by solving a variational stochastic control problem based on the pathwise Zakai equation.
DiffeoMorph learns distributed agent protocols to morph into complex 3D shapes from minimal initial conditions via equivariant GNNs and rotation-invariant Zernike loss.
A new shared video-image tokenizer enables large language models to surpass diffusion models on standard visual generation benchmarks.
Imagen Video generates high-definition text-conditional videos via a cascade of base and super-resolution diffusion models, achieving high fidelity and controllability.
Imagen achieves state-of-the-art photorealistic text-to-image generation by scaling a text-only pretrained T5 language model within a diffusion framework, reaching FID 7.27 on COCO without training on it.
A diffusion model for video generation extends image architectures with joint image-video training and improved conditional sampling, delivering first large-scale text-to-video results and state-of-the-art performance on video prediction and unconditional generation benchmarks.
Progressive distillation halves sampling steps repeatedly in diffusion models, reaching 4 steps with FID 3.0 on CIFAR-10 from 8192-step samplers.
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
A variational method learns a neural approximation to the conditional backward-in-time score of the posterior SDE, inducing an ELBO for joint smoothing and parameter learning from sparse data.
NSP model fuses satellite and gauge data with neural processes and SDEs, outperforming 13 baselines and JAXA's operational product on a new 43k-sample US benchmark across six metrics.
MIOFlow 2.0 learns stochastic cellular trajectories from transcriptomics data via neural SDEs, unbalanced optimal transport for growth, and a joint latent space unifying gene expression with spatial features.
Stein Diffusion Guidance corrects approximate posteriors in diffusion sampling via a Stein variational mechanism and surrogate SOC objective to enable effective guidance beyond high-density regimes.
Neural mean-field games integrate mean-field game theory with neural SDEs to learn strategic interactions from data in a model-free way, demonstrated on games and viral dynamics.
An extension of the finite expression method using TranNet-initialized shallow neural operators is proposed as an effective solver for high-dimensional partial differential equations.
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Variational Inference for L\'evy Process-Driven SDEs via Neural Tilting
A new variational inference method uses neural networks to tilt Lévy measures, enabling scalable posterior inference for jump processes while preserving their discontinuous structure.
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The finite expression method for turbulent dynamics with high-order moment recovery
A two-stage symbolic regression plus generative model framework recovers governing interaction terms and forcing in stochastic triad models while accurately predicting statistical moments up to order five.
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Pathwise Learning of Stochastic Dynamical Systems with Partial Observations
A neural path estimation approach learns the filtering posterior path measure for stochastic dynamical systems from noisy partial observations by solving a variational stochastic control problem based on the pathwise Zakai equation.
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DiffeoMorph: Learning to Morph 3D Shapes Using Differentiable Agent-Based Simulations
DiffeoMorph learns distributed agent protocols to morph into complex 3D shapes from minimal initial conditions via equivariant GNNs and rotation-invariant Zernike loss.
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Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
A new shared video-image tokenizer enables large language models to surpass diffusion models on standard visual generation benchmarks.
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Imagen Video: High Definition Video Generation with Diffusion Models
Imagen Video generates high-definition text-conditional videos via a cascade of base and super-resolution diffusion models, achieving high fidelity and controllability.
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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Imagen achieves state-of-the-art photorealistic text-to-image generation by scaling a text-only pretrained T5 language model within a diffusion framework, reaching FID 7.27 on COCO without training on it.
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Video Diffusion Models
A diffusion model for video generation extends image architectures with joint image-video training and improved conditional sampling, delivering first large-scale text-to-video results and state-of-the-art performance on video prediction and unconditional generation benchmarks.
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Progressive Distillation for Fast Sampling of Diffusion Models
Progressive distillation halves sampling steps repeatedly in diffusion models, reaching 4 steps with FID 3.0 on CIFAR-10 from 8192-step samplers.
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RT-Transformer: The Transformer Block as a Spherical State Estimator
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
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Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
A variational method learns a neural approximation to the conditional backward-in-time score of the posterior SDE, inducing an ELBO for joint smoothing and parameter learning from sparse data.
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Neural Stochastic Processes for Satellite Precipitation Refinement
NSP model fuses satellite and gauge data with neural processes and SDEs, outperforming 13 baselines and JAXA's operational product on a new 43k-sample US benchmark across six metrics.
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MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data
MIOFlow 2.0 learns stochastic cellular trajectories from transcriptomics data via neural SDEs, unbalanced optimal transport for growth, and a joint latent space unifying gene expression with spatial features.
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Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
Stein Diffusion Guidance corrects approximate posteriors in diffusion sampling via a Stein variational mechanism and surrogate SOC objective to enable effective guidance beyond high-density regimes.
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Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations
Neural mean-field games integrate mean-field game theory with neural SDEs to learn strategic interactions from data in a model-free way, demonstrated on games and viral dynamics.
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Finite Expression Method with TranNet-based Function Learning for High-Dimensional Partial Differential Equations
An extension of the finite expression method using TranNet-initialized shallow neural operators is proposed as an effective solver for high-dimensional partial differential equations.