Topic models have been applied to everything from books to newspapers to social media
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …

Personalized recommendation models (RecSys) are one of the most popular machine
learning workload serviced by hyperscalers. A critical challenge of training RecSys is its …

The explosive growth in data volume and the availability of cheap computing resources
have sparked increasing interest in Big learning, an emerging subfield that studies scalable …

Many machine learning methods involve iterative optimization and are amenable to a variety
of alternate formulations. Many currently popular formulations for some machine learning …

Large-scale topic models serve as basic tools for feature extraction and dimensionality
reduction in many practical applications. As a natural extension of flat topic models …

Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language
processing and machine learning. Most approaches to training the model rely on iterative …

Topics have proved to be a valuable source of information for exploring, discovering,
searching and representing the contents of text corpora. They have also been useful for …

Recently, artificial intelligence has become more prevalent due to the combination of more
data, faster processing power, and more powerful algorithms. AI technology has been …

Abstract Analysis of online user-generated reviews has attracted extensive attention with
broad applications in recent years. However, the high-volume and low-value density of …

Topic modeling is a significant branch of natural language processing and machine learning
focused on inferring the generative process of text. Traditionally, algorithms for estimating …