Google Brain Residency Program
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In October 2015 we launched the Google Brain Residency, a 12-month program focused on jumpstarting a career for those interested in machine learning and deep learning research. This program is an opportunity to get hands on experience using the state-of-the-art infrastructure available at Google, and offers the chance to work alongside top researchers within the Google Brain team.
Our first group of residents arrived in June 2016, working with researchers on problems at the forefront of machine learning. The wide array of topics studied by residents reflects the diversity of the residents themselves — some come to the program as new graduates with degrees ranging from BAs to Ph.Ds in computer science to physics and mathematics to biology and neuroscience, while other residents come with years of industry experience under their belts. They all have come with a passion for learning how to conduct machine learning research.
The breadth of research being done by the Google Brain Team along with resident-mentorship pairing flexibility ensures that residents with interests in machine learning algorithms and reinforcement learning, natural language understanding, robotics, neuroscience, genetics and more, are able to find good mentors to help them pursue their ideas and publish interesting work. And just seven months into the program, the Residents are already making an impact in the research field.
To date, Google Brain Residents have submitted a total of 21 papers to leading machine learning conferences, spanning topics from enhancing low resolution images to building neural networks that in turn design novel, task specific neural network architectures. Of those 21 papers, 5 were accepted in the recent BayLearn Conference (two of which, “Mean Field Neural Networks” and “Regularizing Neural Networks by Penalizing Their Output Distribution’’, were presented in oral sessions), 2 were accepted in the NIPS 2016 Adversarial Training workshop, and another in ISMIR 2016 (see the full list of papers, including the 14 submissions to ICLR 2017, after the figures below).
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