Hossein Adeli

Research Scientist

Learning to Attend in Brains and Machines

  Tuesday, 25 December 2018
  16:00 - 19:00
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Attention has become an important component of many recent machine learning and Deep Neural Network (DNN) models. Even though the conceptualization of attention in these models is an approximation of attention mechanism in the brain, it improves the models in both accuracy and interpretability. This points to the potential benefits that building more biologically plausible attention and information routing mechanisms could have for future models.

In this workshop, I will introduce some of the relevant literature in Cognitive and Neural models of visual attention. I will also review recent DNN models using attention mechanism for different tasks, ranging from object recognition to caption generation. I will end by making connections between the different perspectives and provide examples of how they can be mutually beneficial.


Hossein Adeli is a research scientist at Stony Brook University in the departments of Psychology and Computer Science. He is interested in understanding the role of attention control and selective routing of information in both Cognitive and Computational systems. His research interests also include developing Machine learning and AI techniques for different applications inspired by Cognitive and Neural Systems. 
He received his PhD in Cognitive Science from Stony Brook University working in Eye Cognition and Computer Vision Labs. He also has a master's degree in Computer Science and received his bachelors degree in Electrical Engineering form Sharif University of Technology.