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Found 12 papers, 3 papers with code
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks.
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Autoencoders and their variants are among the most widely used models in representation learning and generative modeling.
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
To address this issue, several methods based on surrogate models have been proposed to speed up the inference process.
Optimal Experimental Design for Mathematical Models of Hematopoiesis
In this work, we aim to uncover the underlying mechanisms in hematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution.
Methodology Quantitative Methods Applications
Conjoined Dirichlet Process
Biclustering is a class of techniques that simultaneously clusters the rows and columns of a matrix to sort heterogeneous data into homogeneous blocks.
Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method
We show that our method leads to substantially higher accuracy rate for neural decoding and allows to discover novel biological phenomena by providing a clear latent representation of the decoding process.
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction
We reframe linear dimensionality reduction as a problem of Bayesian inference on matrix manifolds.
Variational Hamiltonian Monte Carlo via Score Matching
Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC).
Sampling constrained probability distributions using Spherical Augmentation
In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space.
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process.
Dependent Matérn Processes for Multivariate Time Series
For the challenging task of modeling multivariate time series, we propose a new class of models that use dependent Mat\'ern processes to capture the underlying structure of data, explain their interdependencies, and predict their unknown values.
Split Hamiltonian Monte Carlo
With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics.
Computation
