Developed by Professor Sanjay Mehrotra, the Sliding Scale AdaptiVe Expedited (SAVE) algorithm could improve organ allocation ...

Researchers have proposed a personalized longitudinal motion planning policy for intelligent vehicles that combines reinforcement learning with imitation learning. The approach is designed to reduce ...

This valuable computational study presents a conceptually simple and biologically plausible reinforcement-learning framework for motor learning based on policy-gradient methods. The evidence ...

Modern economic history has largely been dominated by the puzzle of human capital. How do growing populations continuously raise skill levels as part of a cycle of rising productivity and living ...

Billionaire entrepreneur Mark Cuban cautioned that social media algorithms, rather than candidates' policies or personalities, could have the greatest influence on voter behavior in the upcoming 2026 ...

Motivated by "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. In this project: Implement three state-of-art continous deep ...

While the creation of this new entity marks a big step toward avoiding a U.S. ban, as well as easing trade and tech-related tensions between Washington and Beijing, there is still uncertainty ...

Abstract: Recent developments in cyber-physical systems have increased the importance of maximizing the freshness of the information about the physical environment. However, optimizing the access ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...