Pie & AI: Vienna – Continuous-time recurrent neural networks

Hi,

Welcome to our first online Pie & AI Event!

Pie & AI is a series of deeplearning.ai meetups independently hosted by community groups. This event is hosted by the Vienna Data Science Group.

There are a few steps to complete registration, please carefully follow the next steps:

(2) Please complete your registration on the deeplearning.ai signup form here: https://docs.google.com/forms/d/e/1FAIpQLSfO_6MQv0B95fJjGwgKGKQCvUtoeY4JEk7LofZE8qGqdBxyKQ/viewform .

After the event, we will provide a limited course promo code for attendees who sign up through the form and complete a post-event survey sent by deeplearning.ai after the event. The code is for 50% off first-month subscription to any of deeplearning.ai’s courses on Coursera.

DESCRIPTION

State-of-the-art time-series prediction with continuous-time recurrent neural networks.

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.

OUR SPEAKER

Ramin Hasani, PhD, Machine Learning Scientist at TU Wien, expert in robotics, including previously being a scholar MIT CSAL, presents technical aspects of continuous-time neural networks.

https://www.linkedin.com/in/raminhasani/ https://repositum.tuwien.ac.at/obvutwhs/download/pdf/4937341

AGENDA

17:00 – Introduction and Greeting video by Andrew Ng

17:15 – Main Talk

17:45 – Q&A