Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While
prior research has explored many different ways of reducing DNN training time, the impact of …

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

Transformer models have achieved state-of-the-art performance on various domains of
applications and gradually becomes the foundations of the advanced large deep learning …

End-to-end AutoML has attracted intensive interests from both academia and industry which
automatically searches for ML pipelines in a space induced by feature engineering …

Slice finding---a recent work on debugging machine learning (ML) models---aims to find the
top-K data slices (eg, conjunctions of predicates such as gender female and degree PhD) …

With the rapid adoption of machine learning (ML), a number of domains now use the
approach of fine tuning models which were pre-trained on a large corpus of data. However …

Deep learning (DL) is growing in popularity for many data analytics applications, including
among enterprises. Large business-critical datasets in such settings typically reside in …

Deep learning (DL) is gaining popularity across many domains thanks to tools such as
TensorFlow and easier access to GPUs. But building large-scale DL applications is still too …

The ever-growing demand and complexity of machine learning are putting pressure on
hyper-parameter tuning systems: while the evaluation cost of models continues to increase …

Training wide and deep neural networks (DNNs) require large amounts of storage resources
such as memory because the intermediate activation data must be saved in the memory …