[NeurIPS 2019 Highlight] Martin Schrimpf @ MIT: Brain-Like Object Recognition with Recurrent ANNs
Aug 14, 20206 views
This episode is an interview with Martin Schrimpf Ph.D. student in Brain and Cognitive Sciences at MIT BCS. He shared highlights from his paper Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs, which was accepted for oral presentation at NeurIPS 2019 conference. Learn more at Robin.ly: http://bit.ly/2LQuHGF This paper proposed a quantitative collaboration between neuroscience and machine learning by representing a brain score that allows you to compare models with the brain and developing a brain-like model, CORnet-S, which transforms deep networks into much more shallow networks with recurrence. This research will help build architectures and networks that are more like the brain and improve energy efficiency for computing. Martin's main interest is in bridging Machine Learning and Neuroscience with a focus on building deep neural network models of the brain’s ventral stream that are more human-like in their behavior as well as their internals. His previous work includes research in computer vision at Harvard, and natural language processing and reinforcement learning at Salesforce.