Save the date: 10th of May. Location: Impact Hub Amsterdam
Join us for the first edition of “Deep Learning & AI” talks, a series of events in which we will discuss state of the art developments in deep learning, machine learning and computer vision.
Our first edition takes place at Impact Hub Amsterdam, a cool community space located next to Oosterpark.
Looking forward to seeing you there!
* Please note: the presentations will be highly technical.
Free entrance but the event is already fully booked. Sign up on the waiting list here
Here’s the agenda of the event:
18:00 – 18:45: Doors open + Pizza
18:45 – 19:00: Quick Intro
19:00 – 19:30: “Data-Efficient Deep Learning with G-CNNs” by Taco Cohen
19:30 – 20:00: “Deep Learning on Graphs with Graph Convolutional Networks” by Thomas Kipf
Break 20:00 – 20:10
20:10 – 20:40: “Tracking by Natural Language Specification” by Efstratios Gavves
20:40 – 21:10: Q & A Session
21:10 – 22:30: Drinks and networking
About the speakers:
Taco Cohen is a PhD student at the University of Amsterdam, supervised by prof. Max Welling, and a co-founder at Scyfer. He holds a BSc in computer science from Utrecht University and a MSc in artificial intelligence from the University of Amsterdam (both cum laude). His research is focussed on understanding deep neural networks and the representations they learn, as well as improving the statistical efficiency of deep learning by exploiting symmetries and invariances. He has done internships at Google Deepmind and OpenAI. In 2014, he won the university-wide UvA Thesis prize. In 2017, he was awarded the prestigious Google PhD Fellowship.
Thomas Kipf is a second-year PhD student in deep learning for network analysis at the University of Amsterdam, supervised by Prof. Max Welling. His main area of interest is large-scale inference for structured data and semi-supervised learning. He further explores topics in reasoning and multi-agent reinforcement learning. His formal background is in physics (M.Sc. hons. 2016, B.Sc. 2014 at FAU). During his studies, he has had exposure to a number of fields and—after a short interlude in neuroscience-related research at the Max Planck Institute for Brain Research—eventually developed a deep interest in machine learning.