Our paper 'Variational Autoencoders Recover PCA Directions (by Accident)' got accepted at CVPR 2019! We are looking forward to present it as a poster in June at Long Beach!
The paper elaborates on the close connection between Variational Autoencoders and the well known PCA algorithm in terms of their alignment of the latent space.
presentation of our L4 Paper
We had a busy 2h poster presentation with many interested visitors
Scientists develop new machine learning method that can make robots safer - New method provides simpler and more intuitive models of physical situations
Understanding how a robot will react under different conditions is essential to guaranteeing its safe operation. But how do you know what will break a robot without actually damaging it? A new method developed by scientists at the Institute of Science and Technology Austria and the Max Planck Institute for Intelligent Systems is the first machine learning method that can use observations made under safe conditions to make accurate predictions for all possible conditions governed by the same physical dynamics. Especially designed for real-life situations, their method provides simple, interpretable descriptions of the underlying physics. The researchers will present their findings tomorrow at this year’s prestigious International Conference for Machine Learning (ICML).
A prestigious junior scientist fellowship is awarded to the MPI-IS researcher