9 citations · 15 across the 7 of their papers we have counts for
7 papers
Breaking through the classical Shannon entropy limit: A new frontier through logical semantics
Luis A. Lastras, Barry M. Trager, Jonathan Lenchner +4
Information theory has provided foundations for the theories of several application areas critical for modern society, including communications, computer storage, and AI. A key asp…
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
Weiqin Chen, Mark S. Squillante, Chai Wah Wu +1
We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as…
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation
Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru +12
Numerical computation is essential to many areas of artificial intelligence (AI), whose computing demands continue to grow dramatically, yet their continued scaling is jeopardized…
Stable iterative refinement algorithms for solving linear systems
Chai Wah Wu, Mark S. Squillante, Vasileios Kalantzis +1
Iterative refinement (IR) is a popular scheme for solving a linear system of equations based on gradually improving the accuracy of an initial approximation. Originally developed t…
The Role of Masks in Mitigating Viral Spread on Networks
Yurun Tian, Anirudh Sridhar, Chai Wah Wu +4
Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In…
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies
Yingdong Lu, Mark S. Squillante, Chai Wah Wu
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framew…
On the Control of Density-Dependent Stochastic Population Processes with Time-Varying Behavior
Yingdong Lu, Mark Squillante, Chai Wah Wu
The study of density-dependent stochastic population processes is important from a historical perspective as well as from the perspective of a number of existing and emerging appli…