162 citations · 579 across the 17 of their papers we have counts for
18 papers
Laser: Latent Set Representations for 3D Generative Modeling
Pol Moreno, Adam R. Kosiorek, Heiko Strathmann +6
NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint. NeRF requires training on a large number of views that fully cover a…
Score-Based Diffusion meets Annealed Importance Sampling
Arnaud Doucet, Will Grathwohl, Alexander G. D. G. Matthews +1
More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation. It relies on a seque…
Neural Variational Gradient Descent
Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann
Particle-based approximate Bayesian inference approaches such as Stein Variational Gradient Descent (SVGD) combine the flexibility and convergence guarantees of sampling methods wi…
Persistent Message Passing
Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell +1
Graph neural networks (GNNs) are a powerful inductive bias for modelling algorithmic reasoning procedures and data structures. Their prowess was mainly demonstrated on tasks featur…
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran +4
We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering. In contrast to NeRF, our model takes into accou…
Meta-Learning Mean Functions for Gaussian Processes
Vincent Fortuin, Heiko Strathmann, Gunnar Rätsch
When fitting Bayesian machine learning models on scarce data, the main challenge is to obtain suitable prior knowledge and encode it into the model. Recent advances in meta-learnin…