Underrated ML

Sara Hooker & Sean Hooker

4,2 (5)
TECHNOLOGIES
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Listen along as we try and dissect various Machine Learning papers that just haven't got the love and attention they deserve.Twitter: https://twitter.com/underrated_mlVoting Page: https://forms.gle/97MgHvTkXgdB41TC8

26/07/2022

Strongly typed RNNs and morphogenesis

We conclude season one of Underrated ML by having Stephen Merity on as our guest. Stephen has worked at various institutions such as MetaMind and Salesforce ohana, Google Sydney, Freelancer.com, the Schwa Lab at the University of Sydney, the team at Grok Learning, the non-profit Common Crawl, and IACS @ Harvard. He also holds a Bachelor of Information Technology from the University of Sydney and a Master of Science in Computational Science and Engineering from Harvard University.In this weeks...

1 h 34 min
26/07/2022

Language independence and material properties

This week we are joined by Sebastian Ruder. He is a research scientist at DeepMind, London. He has also worked at a variety of institutions such as AYLIEN, Microsoft, IBM's Extreme Blue, Google Summer of Code, and SAP. These experiences were completed in tangent with his studies which included studying Computational Linguistics at the University of Heidelberg, Germany and at Trinity College, Dublin before undertaking a PhD in Natural Language Processing and Deep Learning at the Insight Resear...

1 h 34 min
26/07/2022

Energy functions and shortcut learning

This week we are joined by Kyunghyun Cho. He is an associate professor of computer science and data science at New York University, a research scientist at Facebook AI Research and a CIFAR Associate Fellow. On top of this he also co-chaired the recent ICLR 2020 virtual conference.We talk about a variety of topics in this weeks episode including the recent ICLR conference, energy functions, shortcut learning and the roles popularized Deep Learning research areas play in answering the question ...

1 h 29 min
26/07/2022

The importance of certain layers in DNNs

This week we are joined by Ari Morcos. Ari is a research scientist at Facebook AI Research (FAIR) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, he has worked on a variety of topics, including understanding the lottery ticket hypothesis, self-supervised learning, the mechanisms underlying common regularizers, and the properties predictive of ...

57 min
26/07/2022

Interestingness predictions and getting to grips with data privacy

This week we are joined by Naila Murray. Naila obtained a B.Sc. in Electrical Engineering from Princeton University in 2007. In 2012, she received her PhD from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined NAVER LABS Europe (then Xerox Research Centre Europe) in January 2013, working on topics including fine-grained visual categorization, image retrieval, and visual attention. From 2015 to 2019 she led the computer vision team at NLE. She cu...

1 h 9 min
26/07/2022

Pooling Layers and learning from Brains

This week we take a look at the need for pooling layers within CNNs as well as discussing the regularization of CNNs using large-scale neuroscience data.We are also very pleased to have Rosanne Liu join us on the show. Rosanne is a senior research scientist and a founding member of Uber AI. She is interested in making neural networks a better place and also currently runs a deep learning reading group called "Deep Learning: Classics and Trends".Rosanne Liu Twitter: https://twitter.com/savvyrl...

1 h 22 min
26/07/2022

Understanding a microprocessor and the evolution of hardware

This week we are joined by Julius Adebayo. Julius is a CS PhD student at MIT, interested in safe deployment of ML based systems as it relates to privacy/security, interpretability, fairness and robustness.He is motivated by the need to ensure that ML based systems demonstrate safe behaviour when deployed.On this weeks episode we discuss how the evolution of hardware has progressed overtime and what that means for deep learning research. We also analyse how microprocessors can aid developments...

1 h 11 min
26/07/2022

Metaphor generation and ML for child welfare

We open season two of Underrated ML with Anna Huang on the show. Anna Huang is a Research Scientist at Google Brain, working on the Magenta project. Her research focuses on designing generative models to make creating music more approachable. She is the creator of Music Transformer and also the ML model Coconet that powered Google’s first AI Doodle the Bach Doodle.She holds a PhD in computer science from Harvard University and was a recipient of the NSF Graduate Research Fellowship. She spent...

1 h 14 min
19/06/2022

Abstract Reasoning and Peer Reviews

This weeks episode we take a look at Abstract Reasoning within Neural Networks as well as discussing the current review system surrounding ML papers. We are also very happy to have Jacob Buckman join us on the podcast this week. Jacob is currently undertaking a PhD at Mila having previously been a researcher at Google Brain with Sara Hooker. His main research interests lie in deep reinforcement learning with a particular focus on sample-efficiency. Please let us know who you thought presented...

1 h 15 min
19/06/2022

Xenobots and Critical Learning Periods

Have a listen to the first ever Underrated ML podcast! We'll walk you through two papers which we found really interesting followed by a few questions and then finally finishing with our verdict on what we believe was the most underrated paper!Links to the papers can be found below.Critical Learning Periods in Deep Neural Networks - https://arxiv.org/abs/1711.08856A scalable pipeline for designing reconfigurable organisms - https://www.pnas.org/content/117/4/1853

1 h 5 min

10 épisodes

4,2

sur 5

5 notes

Création

Sara Hooker & Sean Hooker
Années d’activité

2020 - 2022
Épisodes

10
Classification

Tous publics
Site web de l’émission

Underrated ML

Underrated ML

Épisodes

Strongly typed RNNs and morphogenesis

Language independence and material properties

Energy functions and shortcut learning

The importance of certain layers in DNNs

Interestingness predictions and getting to grips with data privacy

Pooling Layers and learning from Brains

Understanding a microprocessor and the evolution of hardware

Metaphor generation and ML for child welfare

Abstract Reasoning and Peer Reviews

Xenobots and Critical Learning Periods

Notes et avis

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