Leyla Isik | The Center for Brains, Minds & Machines
Leyla is a postdoctoral researcher co-advised by Nancy Kanwisher and Gabriel Kreiman. She is interested in understanding how the brain extracts visual and social information (such as who you are looking at, what that person is doing, and who they are interacting with) from complex scenes. She studies this problem using a combination of human neuroimaging, intracranial recordings, and machine learn
Andrei Barbu | The Center for Brains, Minds & Machines
People CBMM is committed to developing and supporting new interdisciplinary collaborations that will further our understanding of intelligence.
Boris Katz | The Center for Brains, Minds & Machines
Coordinator for Technology and Knowledge Transfer, Research Module Co-Leader Boris Katz is a Principal Research Scientist and Head of the InfoLab Group at MIT’s Computer Science and Artificial Intelligence Laboratory. His research interests include natural language understanding and generation, intelligent information access, knowledge representation, human computer interaction, and event recognit
Elizabeth Spelke | The Center for Brains, Minds & Machines
People CBMM is committed to developing and supporting new interdisciplinary collaborations that will further our understanding of intelligence.
Have We Missed Half of What the Neocortex Does? Allocentric Location as the Basis of Perception | The Center for Brains, Minds & Machines
CBMM, NSF STC » Have We Missed Half of What the Neocortex Does? Allocentric Location as the Basis of Perception Video CBMM videos marked with a have an interactive transcript feature enabled, which appears below the video when playing. Viewers can search for keywords in the video or click on any word in the transcript to jump to that point in the video. When searching, a dark bar with white verti
Deep Convolutional Networks are Hierarchical Kernel Machines | The Center for Brains, Minds & Machines
We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, respectively. Under the assumption of normalized inputs, we show that appropriate linear combinations of rectifying nonlinearities are equivalent to radial kernels. If pooling is p
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