| Title | MLT Talk: "Infusing Structure into Machine Learning Algorithms" |
|---|---|
| Date | 2019年4月23日（火）April 23, 2019, 19:30－20:45 |
| Venue | 東京大学本郷キャンパス 国際学術総合研究棟地下１階・第５教室 [地図] Lecture Hall No. 5, International Academic Research Building, University of Tokyo (Hongo Campus) [MAP] |
| Speaker | Anima Anandkumar (Director of ML Research at NVIDIA and Bren Professor at Caltech) |
| Information | 
主催：Machine Learning Tokyo 共催：東京大学政策評価研究教育センター (CREPE) Hosted by Machine Learning Tokyo and co-organized by CREPE, The University of Tokyo |
| Abstract | 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. |
| Language | 講演は英語で行われます（Lecture in English） |