The last 10 years have witnessed the growth of artificial intelligence into different research
areas, emerging as a vibrant discipline with the capacity to process large amounts of …

In recent years, numerous attempts have been made to integrate sliding mode control (SMC)
and neural networks (NN) in order to leverage the advantages of both methods while …

The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques …

Multi-agent predictive modeling is an essential step for understanding physical, social and
team-play systems. Recently, Interaction Networks (INs) were proposed for the task of …

In this paper we discuss policy iteration methods for approximate solution of a finite-state
discounted Markov decision problem, with a focus on feature-based aggregation methods …

We present a novel approach to efficiently compute tight non-convex enclosures of the
image through neural networks with ReLU, sigmoid, or hyperbolic tangent activation …

With the recent advancements in machine learning theory, many commercial embedded
microprocessors use neural network (NN) models for a variety of signal processing …

This article focuses on the recent advances in the field of reinforcement learning (RL) as well
as the present state–of–the–art applications in games. First, we give a general panorama of …

With the recent advancements in machine learning (ML) theory, a lot of energy-efficient
neural network (NN) accelerators have been developed. However, their associated side …

The recent breakthrough successes in machine learning are mainly attributed to scale:
namely large-scale attention-based architectures and datasets of unprecedented scale. This …