97 citations · 132 across the 6 of their papers we have counts for
6 papers
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development
Aref Hakimzadeh, Yanbo Xue, Peyman Setoodeh
Endeavors for designing robots with human-level cognitive abilities have led to different categories of learning machines. According to Skinner's theory, reinforcement learning (RL…
Enacted Visual Perception: A Computational Model based on Piaget Equilibrium
Aref Hakimzadeh, Yanbo Xue, Peyman Setoodeh
In Maurice Merleau-Ponty's phenomenology of perception, analysis of perception accounts for an element of intentionality, and in effect therefore, perception and action cannot be v…
Deep Reinforcement Learning-Based Product Recommender for Online Advertising
Milad Vaali Esfahaani, Yanbo Xue, Peyman Setoodeh
In online advertising, recommender systems try to propose items from a list of products to potential customers according to their interests. Such systems have been increasingly dep…
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines
Aidan Kehoe, Peter Wittek, Yanbo Xue +1
We provide a robust defence to adversarial attacks on discriminative algorithms. Neural networks are naturally vulnerable to small, tailored perturbations in the input data that le…
Distributed Training of Deep Learning Models: A Taxonomic Perspective
Matthias Langer, Zhen He, Wenny Rahayu +1
Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster. Developers of DDLS are required to make many decision…
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian +4
Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA r…