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Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder
When solving inverse problems in geophysical imaging, deep generative models (DGMs)
may be used to enforce the solution to display highly structured spatial patterns which are …
may be used to enforce the solution to display highly structured spatial patterns which are …
Deferred neural rendering: Image synthesis using neural textures
The modern computer graphics pipeline can synthesize images at remarkable visual quality;
however, it requires well-defined, high-quality 3D content as input. In this work, we explore …
however, it requires well-defined, high-quality 3D content as input. In this work, we explore …
On the continuity of rotation representations in neural networks
In neural networks, it is often desirable to work with various representations of the same
space. For example, 3D rotations can be represented with quaternions or Euler angles. In …
space. For example, 3D rotations can be represented with quaternions or Euler angles. In …
Deepvoxels: Learning persistent 3d feature embeddings
In this work, we address the lack of 3D understanding of generative neural networks by
introducing a persistent 3D feature embedding for view synthesis. To this end, we propose …
introducing a persistent 3D feature embedding for view synthesis. To this end, we propose …
Stochastic normalizing flows
The sampling of probability distributions specified up to a normalization constant is an
important problem in both machine learning and statistical mechanics. While classical …
important problem in both machine learning and statistical mechanics. While classical …
Learning meaningful representations of protein sequences
How we choose to represent our data has a fundamental impact on our ability to
subsequently extract information from them. Machine learning promises to automatically …
subsequently extract information from them. Machine learning promises to automatically …
Normalizing flows on tori and spheres
Normalizing flows are a powerful tool for building expressive distributions in high
dimensions. So far, most of the literature has concentrated on learning flows on Euclidean …
dimensions. So far, most of the literature has concentrated on learning flows on Euclidean …
CryoDRGN2: Ab initio neural reconstruction of 3D protein structures from real cryo-EM images
Protein structure determination from cryo-EM data requires reconstructing a 3D volume (or
distribution of volumes) from many noisy and randomly oriented 2D projection images. While …
distribution of volumes) from many noisy and randomly oriented 2D projection images. While …
Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder
In this paper, we propose a new method to perform data augmentation in a reliable way in
the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based …
the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based …
Coordinate Independent Convolutional Networks--Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds
Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …