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Found 19 papers, 11 papers with code
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering.
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples.
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
Kernel Adaptive Metropolis-Hastings
A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the purpose of sampling from a target distribution with strongly nonlinear support.
Score-Based Diffusion meets Annealed Importance Sampling
To obtain an importance sampling estimate of the marginal likelihood, AIS introduces an extended target distribution to reweight the Markov chain proposal.
Learning deep kernels for exponential family densities
The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching.
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC).
A Kernel Test of Goodness of Fit
Our test statistic is based on an empirical estimate of this divergence, taking the form of a V-statistic in terms of the log gradients of the target density and the kernel.
Neural Variational Gradient Descent
Particle-based approximate Bayesian inference approaches such as Stein Variational Gradient Descent (SVGD) combine the flexibility and convergence guarantees of sampling methods with the computational benefits of variational inference.
Kernel Sequential Monte Carlo
As KSMC does not require access to target gradients, it is particularly applicable on targets whose gradients are unknown or prohibitively expensive.
Efficient and principled score estimation with Nyström kernel exponential families
We propose a fast method with statistical guarantees for learning an exponential family density model where the natural parameter is in a reproducing kernel Hilbert space, and may be infinite-dimensional.
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
The methodology is reviewed on well-known examples such as the parameters in Ising models, the posterior for Fisher-Bingham distributions on the $d$-Sphere and a large-scale Gaussian Markov Random Field model describing the Ozone Column data.
Unbiased Bayes for Big Data: Paths of Partial Posteriors
A key quantity of interest in Bayesian inference are expectations of functions with respect to a posterior distribution.
Scalable Gaussian Processes on Discrete Domains
We explore different techniques for selecting inducing points on discrete domains, including greedy selection, determinantal point processes, and simulated annealing.
Optimal kernel choice for large-scale two-sample tests
A means of parameter selection for the two-sample test based on the MMD is proposed.
Meta-Learning Mean Functions for Gaussian Processes
When it comes to meta-learning in Gaussian process models, approaches in this setting have mostly focused on learning the kernel function of the prior, but not on learning its mean function.
Persistent Message Passing
Graph neural networks (GNNs) are a powerful inductive bias for modelling algorithmic reasoning procedures and data structures.
Laser: Latent Set Representations for 3D Generative Modeling
NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint.
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
