It is well known that in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its accurate approximation by Markov chain Monte Carlo methods requires huge computational costs. As an alternative, a tractable approximation method, called the variational Bayes (VB) approach, has recently been proposed and ...

In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective ...

A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate t...

In the main text we derived Adversarial Variational Bayes (AVB) and demonstrated its usefulness both for black-box Variational Inference and for learning latent variable models. This document contains proofs that were omitted in the main text as well as some further details about the experiments and additional results.

Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such explain decisions in response to inputs drawn from the test set. However, set may have few examples that trigger some model behaviors, as high-confidence failures or ambiguous classifications. To address these challenges, we introduce a flexible inspection...

One of the benefits of belief networks and influence diagrams is that so much knowl­ edge is captured in the graphical structure. In particular, statements of conditional irrel­ evance (or independence) can be verified in time linear in the size of the graph. To re­ solve a particular inference query or decision problem, only some of the possible states and probability distributions must be spe...

We employed a multilevel hierarchical Bayesian model in the task of exploiting relevant interactions among high cardinality attributes in a classification problem without overfitting. With this model, we calculate posterior class probabilities for a pattern W combining the observations of W in the training set with prior class probabilities that are obtained recursively from the observations of...