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Found 17 papers, 9 papers with code
Constrained Generative Modeling with Manually Bridged Diffusion Models
We develop a mechanism for combining multiple such constraints so that the resulting multiply-constrained model remains a manual bridge that respects all constraints.
Lifelong Learning of Video Diffusion Models From a Single Video Stream
This work demonstrates that training autoregressive video diffusion models from a single, continuous video stream is not only possible but remarkably can also be competitive with standard offline training approaches given the same number of gradient steps.
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be easy to use, and easy to modify.
Semantically Consistent Video Inpainting with Conditional Diffusion Models
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information across frames.
Nearest Neighbour Score Estimators for Diffusion Generative Models
Score function estimation is the cornerstone of both training and sampling from diffusion generative models.
Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance
The maximum likelihood principle advocates parameter estimation via optimization of the data likelihood function.
RangeAugment: Efficient Online Augmentation with Range Learning
To answer the open question on the importance of magnitude ranges for each augmentation operation, we introduce RangeAugment that allows us to efficiently learn the range of magnitudes for individual as well as composite augmentation operations.
Flexible Diffusion Modeling of Long Videos
We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments.
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Conditional Image Generation by Conditioning Variational Auto-Encoders
We present a conditional variational auto-encoder (VAE) which, to avoid the substantial cost of training from scratch, uses an architecture and training objective capable of leveraging a foundation model in the form of a pretrained unconditional VAE.
Uncertainty in Neural Processes
We aim this work to be a counterpoint to a recent trend in the literature that stresses achieving good samples when the amount of conditioning data is large.
Planning as Inference in Epidemiological Models
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models.
Coping With Simulators That Don't Always Return
Deterministic models are approximations of reality that are easy to interpret and often easier to build than stochastic alternatives.
Amortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
Coping With Simulators That Don’t Always Return
Deterministic models are approximations of reality that are often easier to build and interpret than stochastic alternatives.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
