Data Scientist, Scalarr Inc
Topic: State representation learning and associative memory for intelligent agents
Abstract: Most real-world world tasks are hopelessly complex from the point of view of reinforcement learning mechanisms. In this talk we will give an overview of methods for finding efficient state representations form agent’s observations. We will Bayesian inference with infinite capacity models, and explore links representation learning and the machine learning problem of transfer learning. Then we will present models of neural associative memory and their interplay with machine learning.
About: PhD. in Computer Science and Applied Mathematics. Expert in Artificial Intelligence, Neural Networks, Computational Neuroscience, Machine Learning and optimization. Experience as a Senior Research Fellow in US (University of Massachusetts)