University of California, Berkeley
Three principles of data science: predictability, stability, and computability
In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title. The three principles will be demonstrated in the context of two neuroscience projects and through analytical connections. In particular, the first project adds stability to predictive models used for reconstruction of movies from fMRI brain signals to gain interpretability of the predictive models. The second project uses predictive transfer learning and stable (manifold) deep dream images to characterize the difficult V4 neurons in the primate visual cortex. Our results lend support, to a certain extent, to the resemblance to a primate brain of Convolutional Neural Networks (CNNs).
Refreshments at 3:45pm in Snedecor 2101.