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Found 7 papers, 1 papers with code
On the link between generative semi-supervised learning and generative open-set recognition
More formally, bad-looking samples lie in the complementary space, which is the area between and around the boundaries of the labelled categories within the classifier's embedding space.
A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery
In this paper, we present an extension to LDA that uses a Markov chain to model temporal information.
A Probabilistic Graphical Model Approach to the Structure-and-Motion Problem
We present a means of formulating and solving the well known structure-and-motion problem in computer vision with probabilistic graphical models.
Graph Coloring: Comparing Cluster Graphs to Factor Graphs
We present a means of formulating and solving graph coloring problems with probabilistic graphical models.
Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm
It is in principle possible to convert loopy PGMs to an equivalent tree structure, but this is usually impractical for interesting problems due to exponential blow-up.
Stabilising priors for robust Bayesian deep learning
Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs.
The PAV algorithm optimizes binary proper scoring rules
There has been much recent interest in application of the pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the probabilistic outputs of automatic pattern recognition and machine learning algorithms.
