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Found 7 papers, 1 papers with code
Looking at CTR Prediction Again: Is Attention All You Need?
Click-through rate (CTR) prediction is a critical problem in web search, recommendation systems and online advertisement displaying.
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development
Endeavors for designing robots with human-level cognitive abilities have led to different categories of learning machines.
Enacted Visual Perception: A Computational Model based on Piaget Equilibrium
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 viewed as distinct procedures.
Deep Reinforcement Learning-Based Product Recommender for Online Advertising
This paper provides a comparative study between value-based and policy-based deep RL algorithms for designing recommender systems for online advertising.
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines
We find improvements ranging from 5% to 72% against attacks with Boltzmann machines on the MNIST dataset.
Distributed Training of Deep Learning Models: A Taxonomic Perspective
Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster.
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
We argue that this relates to the fact that we are training a quantum rather than classical Boltzmann distribution in this case.
