Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

Pedestrian path prediction is a very challenging problem because scenes are often crowded
or contain obstacles. Existing state-of-the-art long short-term memory (LSTM)-based …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many domains …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

Natural language generation (NLG) is a critical component of spoken dialogue and it has a
significant impact both on usability and perceived quality. Most NLG systems in common use …

Modeling the properties and functions of DNA sequences is an important, but challenging
task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the …

In this paper, we investigate the problem of learning a machine translation model that can
simultaneously translate sentences from one source language to multiple target languages …

Objective: Patient notes in electronic health records (EHRs) may contain critical information
for medical investigations. However, the vast majority of medical investigators can only …

Language models have traditionally been estimated based on relative frequencies, using
count statistics that can be extracted from huge amounts of text data. More recently, it has …