PIE & AI Vienna / Hosted by VDSG
State-of-the-art time-series prediction with continuous-time recurrent neural networks
Welcome to the first online Pie & AI event of Vienna!
Neural networks with continuous-time hidden state representations have become unprecedentedly popular within the machine learning community. This is due to their strong approximation capability in modeling time-series, their adaptive computation modality, their memory and parameter efficiency. In this talk Ramin will discuss how this family of neural networks work and why they realize attractive degrees of generalizability across different application domains.
Ramin Hasani, PhD, Machine Learning Scientist at University of Vienna, expert in robotics, including previously being a scholar MIT CSAL, presents technical aspects of continuous-time neural networks.
17:00 - Introduction and Greeting video by Andrew Ng
17:15 - Main Talk
17: 45 - Discussion/Q&A
Zoltan C. Toth