Sign up to save your podcasts
Or
Share Algorithmic Reasoning, Graph Neural Nets, AGI and Tips to researchers | Petar Veličković
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Algorithmic Reasoning, Graph Neural Nets, AGI and Tips to researchers | Petar Veličković
Dr. Petar Veličković is a Staff Research Scientist at Googe DeepMind and an Affiliated lecturer at the University of Cambridge. He is known for his research contributions in graph representation learning; particularly graph neural networks and graph attention networks. At DeepMind, he has been working on Neural Algorithmic Reasoning which we talk about more in this podcast. Petar’s research has been featured in numerous media articles and has been impactful in many ways including Google Maps’s improved predictions.
Time stamps
00:00:00 Highlights
00:01:00 Introduction
00:01:50 Entry point in AI
00:03:44 Idea of Graph Attention Networks
00:06:50 Towards AGI
00:09:58 Attention in Deep learning
00:13:15 Attention vs Convolutions
00:20:20 Neural Algorithmic Reasoning (NAR)
00:25:40 End-to-end learning vs NAR
00:30:40 Improving Google Map predictions
00:34:08 Interpretability
00:41:28 Working at Google DeepMind
00:47:25 Fundamental vs Applied side of research
00:50:58 Industry vs Academia in AI Research
00:54:25 Tips to young researchers
01:05:55 Is a PhD required for AI research?
More about Petar: https://petar-v.com/
Graph Attention Networks: https://arxiv.org/abs/1710.10903
Neural Algorithmic Reasoning: https://www.cell.com/patterns/pdf/S2666-3899(21)00099-4.pdf
TacticAI paper: https://arxiv.org/abs/2310.10553
And his collection of invited talks: @petarvelickovic6033
About the Host:
Jay is a PhD student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***
View all episodes
By Jay Shah
5
1414 ratings
Dr. Petar Veličković is a Staff Research Scientist at Googe DeepMind and an Affiliated lecturer at the University of Cambridge. He is known for his research contributions in graph representation learning; particularly graph neural networks and graph attention networks. At DeepMind, he has been working on Neural Algorithmic Reasoning which we talk about more in this podcast. Petar’s research has been featured in numerous media articles and has been impactful in many ways including Google Maps’s improved predictions.
Time stamps
00:00:00 Highlights
00:01:00 Introduction
00:01:50 Entry point in AI
00:03:44 Idea of Graph Attention Networks
00:06:50 Towards AGI
00:09:58 Attention in Deep learning
00:13:15 Attention vs Convolutions
00:20:20 Neural Algorithmic Reasoning (NAR)
00:25:40 End-to-end learning vs NAR
00:30:40 Improving Google Map predictions
00:34:08 Interpretability
00:41:28 Working at Google DeepMind
00:47:25 Fundamental vs Applied side of research
00:50:58 Industry vs Academia in AI Research
00:54:25 Tips to young researchers
01:05:55 Is a PhD required for AI research?
More about Petar: https://petar-v.com/
Graph Attention Networks: https://arxiv.org/abs/1710.10903
Neural Algorithmic Reasoning: https://www.cell.com/patterns/pdf/S2666-3899(21)00099-4.pdf
TacticAI paper: https://arxiv.org/abs/2310.10553
And his collection of invited talks: @petarvelickovic6033
About the Host:
Jay is a PhD student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***