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Found 10 papers, 1 papers with code
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
We thus propose a novel combination of optimization and sampling techniques for approximate Bayesian inference by constructing an IS proposal distribution through the minimization of a forward KL (FKL) divergence.
An inner-loop free solution to inverse problems using deep neural networks
For matrix inversion in the second sub-problem, we learn a convolutional neural network to approximate the matrix inversion, i. e., the inverse mapping is learned by feeding the input through the learned forward network.
Transfer Learning via Latent Factor Modeling to Improve Prediction of Surgical Complications
We aim to create a framework for transfer learning using latent factor models to learn the dependence structure between a larger source dataset and a target dataset.
Fast Second Order Stochastic Backpropagation for Variational Inference
We propose a second-order (Hessian or Hessian-free) based optimization method for variational inference inspired by Gaussian backpropagation, and argue that quasi-Newton optimization can be developed as well.
Parallelizing MCMC with Random Partition Trees
The new algorithm applies random partition trees to combine the subset posterior draws, which is distribution-free, easy to resample from and can adapt to multiple scales.
Modelling Reciprocating Relationships with Hawkes Processes
We present a Bayesian nonparametric model that discovers implicit social structure from interaction time-series data.
Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models
This ability requires a neural code that represents probability distributions and neural circuits that are capable of implementing the operations of probabilistic inference.
Testing a Bayesian Measure of Representativeness Using a Large Image Database
How do people determine which elements of a set are most representative of that set?
Hierarchical Learning of Dimensional Biases in Human Categorization
Specifically, we present a rational model that does not assume dimensions, but learns the same type of dimensional generalizations that people display.
Bayesian Exponential Family PCA
Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data.
