The rise of big data has led to new demands for machine learning (ML) systems to learn
complex models, with millions to billions of parameters, that promise adequate capacity to …

Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

PipeDream is a Deep Neural Network (DNN) training system for GPUs that parallelizes
computation by pipelining execution across multiple machines. Its pipeline parallel …

Language models are generally trained on data spanning a wide range of topics (eg, news,
reviews, fiction), but they might be applied to an a priori unknown target distribution (eg …

How can one build a distributed framework that allows efficient deployment of a wide
spectrum of modern advanced machine learning (ML) programs for industrial-scale …

Abstract Latent Dirichlet Allocation (LDA) is a widely adopted topic model for industrial-
grade text mining applications. However, its performance heavily relies on the collection of …

Most current chatbot engines are designed to reply to user utterances based on existing
utterance-response (or QR) 1 pairs. In this paper, we present DocChat, a novel information …

Recent advancements in natural language processing (NLP) methods have significantly
improved their performance. However, more complex NLP models are more difficult to …

In order to mine latent semantics from text data, word embedding and topic modeling are two
major methodologies in the industry. From a pragmatic perspective, each of these two lines …

Many distributed machine learning (ML) systems adopt the non-synchronous execution in
order to alleviate the network communication bottleneck, resulting in stale parameters that …