Tsvi Achler has a unique background focusing on the neural mechanisms of recognition from a multidisciplinary perspective. He has done extensive work in theory and simulations, human cognitive experiments, animal neurophysiology experiments, and clinical training. He has an applied engineering background, has received bachelor degrees from UC Berkeley in Electrical Engineering, Computer Science and advanced degrees from University of Illinois at Urbana-Champaign in Neuroscience (PhD), Medicine (MD) and worked as a postdoc in Computer Science, and at Los Alamos National Labs, and IBM Research. He now heads his own startup Optimizing Mind whose goal is to provide the next generation of machine learning algorithms.
Thursday - October 18, 2018
Debug and Approve your Deep Networks by Overcoming the Black Box Problem
Deep Learning AI may learn to perform tasks by cheating in unknown and unexpected ways, which may be a liability for the developer. Feedforward networks are the basis of artificial neural networks such as deep, convolution, recurrent, and even machine learning regression methods. However the internal decision processes of feedforward networks are difficult to explain: they are known to be a "black-box". ...
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