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Found 10 papers, 5 papers with code
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
In this work, we build upon these ideas using the score-based interpretation of diffusion models, and explore alternative ways to condition, modify, and reuse diffusion models for tasks involving compositional generation and guidance.
Continuous diffusion for categorical data
Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement.
Maximum Likelihood Training of Score-Based Diffusion Models
Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matching losses.
Ranked #6 on
Image Generation
on ImageNet 32x32
(bpd metric)
SBI -- A toolkit for simulation-based inference
$\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.
On Contrastive Learning for Likelihood-free Inference
Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible.
Neural Spline Flows
A normalizing flow models a complex probability density as an invertible transformation of a simple base density.
Cubic-Spline Flows
A normalizing flow models a complex probability density as an invertible transformation of a simple density.
Autoregressive Energy Machines
We propose the Autoregressive Energy Machine, an energy-based model which simultaneously learns an unnormalized density and computes an importance-sampling estimate of the normalizing constant for each conditional in an autoregressive decomposition.
Sequential Neural Methods for Likelihood-free Inference
Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators.
The Context-Aware Learner
Such reasoning requires learning disentangled representations of data which are interpretable in isolation, but can also be combined in a new, unseen scenario.
