Search Results for author:
Found 7 papers, 3 papers with code
Two-Stage Holistic and Contrastive Explanation of Image Classification
The need to explain the output of a deep neural network classifier is now widely recognized.
Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling
Topic modeling has been one of the most active research areas in machine learning in recent years.
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.
Latent Tree Analysis
Latent tree analysis seeks to model the correlations among a set of random variables using a tree of latent variables.
Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis
The resulting topic model contains a hierarchy of topics so that users can browse the topics from the top level to the bottom level.
Latent Tree Models for Hierarchical Topic Detection
The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below.
Progressive EM for Latent Tree Models and Hierarchical Topic Detection
It is as efficient as the state-of-the-art LDA-based method for hierarchical topic detection and finds substantially better topics and topic hierarchies.
