The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …

The artificial neural network (ANN)–a machine learning technique inspired by the human
neuronal synapse system–was introduced in the 1950s. However, the ANN was previously …

Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …

There is an increasing need to bring machine learning to a wide diversity of hardware
devices. Current frameworks rely on vendor-specific operator libraries and optimize for a …

Distributed training of massive machine learning models, in particular deep neural networks,
via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of …

This paper describes AllenNLP, a platform for research on deep learning methods in natural
language understanding. AllenNLP is designed to support researchers who want to build …

Parallel implementations of stochastic gradient descent (SGD) have received significant
research attention, thanks to its excellent scalability properties. A fundamental barrier when …

Deep learning (DL) training jobs bring some unique challenges to existing cluster
managers, such as unpredictable training times, an all-or-nothing execution model, and …

In this paper, we propose the utterance-level permutation invariant training (uPIT) technique.
uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker …

TensorFlow is an interface for expressing machine learning algorithms, and an
implementation for executing such algorithms. A computation expressed using TensorFlow …