In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

We introduce an exploration bonus for deep reinforcement learning methods that is easy to
implement and adds minimal overhead to the computation performed. The bonus is the error …

Reinforcement learning algorithms rely on carefully engineering environment rewards that
are extrinsic to the agent. However, annotating each environment with hand-designed …

Deep neural networks (DNNs) have recently achieved great success in many visual
recognition tasks. However, existing deep neural network models are computationally …

Abstract The recent" Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a
simple approach to creating sparse networks (keep the large weights) results in models that …

To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most
existing methods prune neurons by only considering the statistics of an individual layer or …

This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained
deep neural network to a mobile platform given a resource budget. While many existing …

Objective. Brain–computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally chosen from a …

In recent years, deep neural networks (DNNs) have received increased attention, have been
applied to different applications, and achieved dramatic accuracy improvements in many …