Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view synthesis …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

We propose a new simple approach for image compression: instead of storing the RGB
values for each pixel of an image, we store the weights of a neural network overfitted to the …

The ability to design functional sequences and predict effects of variation is central to protein
engineering and biotherapeutics. State-of-art computational methods rely on models that …

Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for
many natural language processing (NLP) tasks. The VAE objective consists of two terms,(i) …

We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure
via Neural Radiance Fields (NeRF) and differentiable volume rendering. In contrast to …