Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

There is growing interest in imputing missing data in tabular datasets using deep learning.
Existing deep learning–based imputation models have been commonly evaluated using root …

Linear algebra operations, which are ubiquitous in machine learning, form major
performance bottlenecks. The High-Performance Computing community invests significant …

In recent years, there has been a convergence of Big Data (BD), High Performance
Computing (HPC), and Machine Learning (ML) systems. This convergence is due to the …

Understanding the designs and performance characterizations of hot interconnects on
modern data center and high-performance computing (HPC) clusters is a fruitful research …

Nowadays, GPGPU plays an important role in data centers for Deep Learning training.
However, GPU might not be suitable for many Deep Learning inference applications …

Agriculture presents challenges in automation, especially so in vision systems. Varying
lighting conditions, sporadic diversity, and large amounts of noise create difficulty in …

Customization of a convolutional neural network (CNN) to a specific compute platform
involves finding an optimal pareto state between computational complexity of the CNN and …

The explosive growth of various types of big data and advances in AI technologies have
catalyzed a new type of workloads called multi-modal DNNs. Multi-modal DNNs are capable …

Machine learning (ML) is becoming a powerful tool for a variety of applications where
artificial intelligence solutions are required. A ML benchmark is a standard suite to measure …