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Found 6 papers, 4 papers with code
Score-based Diffusion Models in Function Space
They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Under this framework, we show that minimizing the score-matching loss is equivalent to maximizing a lower bound of the likelihood of the plug-in reverse SDE proposed by Song et al. (2021), bridging the theoretical gap.
Bijective-Contrastive Estimation
In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models.
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary continuous random variable.
Neural Multisensory Scene Inference
For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e. g., by looking at and touching objects.
Geometric GAN
Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other.
