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 …

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 …

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

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 …

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 …

摘要人工智能利用各种优化技术从海量训练样本中学习关键特征或知识以提高解的质量,
这对训练方法提出了更高要求. 然而, 传统单机训练无法满足存储与计算性能等方面的需求 …

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