Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. She was recently nominated to the World Economic Forum's Expert Network consisting of leading experts from academia, business, government, and the media. She has been featured in documentaries by PBS, KPCC, wired magazine, and in articles by MIT Technology review, Forbes, Yourstory, O’Reilly media, and so on. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.
Standard deep-learning algorithms are based on a function-fitting approach that do not exploit any domain knowledge or constraints. This makes them unsuitable in applications that have limited data or require safety or stability guarantees, such as robotics. By infusing structure and physics into deep-learning algorithms, we can overcome these limitations. There are several ways to do this. For instance, we use tensorized neural networks to encode multidimensional data and higher-order correlations. We combine symbolic expressions with numerical data to learn a domain of functions and obtain strong generalization. We combine baseline controllers with learnt residual dynamics to improve landing of quadrotor drones. These instances demonstrate that building structure into ML algorithms can lead to significant gains.
新丸ビル, Room 902
2019年04月22日 午後01:00 - 午後01:50
We are entering the era of AI and the topic has long left the research labs and spilled into newspaper headlines and mainstream policy agendas. Startups, corporates, academia and governments position themselves in order to obtain the best talent, data and solutions in their attempt to get a competitive edge. How has AI developed differently in different ecosystems, form the US, across the pacific to China and in the rest of the world. What is the role of academia; what is the role of the government and its agencies; and what is the role of the industry leaders and the private sector. This experienced and expert panel will address this and many other questions from their different perspectives and roles and offer some amazing insight into their perception on the future of artificial intelligence.
2019年04月23日 午前10:30 - 午前11:30