After an introductory section, this article will focus on four questions: How should the Kyoto
School be defined? What is meant by its central philosophical concept of “absolute …

Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide
interest for many applications. Previous work has developed an $ O (1) $ Metropolis …

Expectation-Maximization (EM) is a popular tool for learning latent variable models, but the
vanilla batch EM does not scale to large data sets because the whole data set is needed at …

Recurrent neural networks, such as long-short term memory (LSTM) networks, are powerful
tools for modeling sequential data like user browsing history (Tan et al., 2016; Korpusik et …

Dual encoder models are ubiquitous in modern classification and retrieval. Crucial for
training such dual encoders is an accurate estimation of gradients from the partition function …

For clustering problems based on the model of mixture probability distribution separation,
we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary …

The increasing popularity of Apache Spark has attracted many users to put their data into its
ecosystem. On the other hand, it has been witnessed in the literature that Spark is slow …

Recently, topic models such as Latent Dirichlet Allocation (LDA) have been widely used in
large-scale web mining. Many large-scale LDA training systems have been developed …

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 popular tool for analyzing discrete count data such as
text and images. Applications require LDA to handle both large datasets and a large number …