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#036 - Max Welling: Quantum, Manifolds & Symmetries in ML
#036 - Max Welling: Quantum, Manifolds & Symmetries in ML
ratings:
Length:
103 minutes
Released:
Jan 3, 2021
Format:
Podcast episode
Description
Today we had a fantastic conversation with Professor Max Welling, VP of Technology, Qualcomm Technologies Netherlands B.V.
Max is a strong believer in the power of data and computation and its relevance to artificial intelligence. There is a fundamental blank slate paradgm in machine learning, experience and data alone currently rule the roost. Max wants to build a house of domain knowledge on top of that blank slate. Max thinks there are no predictions without assumptions, no generalization without inductive bias. The bias-variance tradeoff tells us that we need to use additional human knowledge when data is insufficient.
Max Welling has pioneered many of the most sophistocated inductive priors in DL models developed in recent years, allowing us to use Deep Learning with non-euclidean data i.e. on graphs/topology (a field we now called "geometric deep learning") or allowing network architectures to recognise new symmetries in the data for example gauge or SE(3) equivariance. Max has also brought many other concepts from his physics playbook into ML, for example quantum and even Bayesian approaches.
This is not an episode to miss, it might be our best yet!
Panel: Dr. Tim Scarfe, Yannic Kilcher, Alex Stenlake
00:00:00 Show introduction
00:04:37 Protein Fold from DeepMind -- did it use SE(3) transformer?
00:09:58 How has machine learning progressed
00:19:57 Quantum Deformed Neural Networks paper
00:22:54 Probabilistic Numeric Convolutional Neural Networks paper
00:27:04 Ilia Karmanov from Qualcomm interview mini segment
00:32:04 Main Show Intro
00:35:21 How is Max known in the community?
00:36:35 How Max nurtures talent, freedom and relationship is key
00:40:30 Selecting research directions and guidance
00:43:42 Priors vs experience (bias/variance trade-off)
00:48:47 Generative models and GPT-3
00:51:57 Bias/variance trade off -- when do priors hurt us
00:54:48 Capsule networks
01:03:09 Which old ideas whould we revive
01:04:36 Hardware lottery paper
01:07:50 Greatness can't be planned (Kenneth Stanley reference)
01:09:10 A new sort of peer review and originality
01:11:57 Quantum Computing
01:14:25 Quantum deformed neural networks paper
01:21:57 Probabalistic numeric convolutional neural networks
01:26:35 Matrix exponential
01:28:44 Other ideas from physics i.e. chaos, holography, renormalisation
01:34:25 Reddit
01:37:19 Open review system in ML
01:41:43 Outro
Max is a strong believer in the power of data and computation and its relevance to artificial intelligence. There is a fundamental blank slate paradgm in machine learning, experience and data alone currently rule the roost. Max wants to build a house of domain knowledge on top of that blank slate. Max thinks there are no predictions without assumptions, no generalization without inductive bias. The bias-variance tradeoff tells us that we need to use additional human knowledge when data is insufficient.
Max Welling has pioneered many of the most sophistocated inductive priors in DL models developed in recent years, allowing us to use Deep Learning with non-euclidean data i.e. on graphs/topology (a field we now called "geometric deep learning") or allowing network architectures to recognise new symmetries in the data for example gauge or SE(3) equivariance. Max has also brought many other concepts from his physics playbook into ML, for example quantum and even Bayesian approaches.
This is not an episode to miss, it might be our best yet!
Panel: Dr. Tim Scarfe, Yannic Kilcher, Alex Stenlake
00:00:00 Show introduction
00:04:37 Protein Fold from DeepMind -- did it use SE(3) transformer?
00:09:58 How has machine learning progressed
00:19:57 Quantum Deformed Neural Networks paper
00:22:54 Probabilistic Numeric Convolutional Neural Networks paper
00:27:04 Ilia Karmanov from Qualcomm interview mini segment
00:32:04 Main Show Intro
00:35:21 How is Max known in the community?
00:36:35 How Max nurtures talent, freedom and relationship is key
00:40:30 Selecting research directions and guidance
00:43:42 Priors vs experience (bias/variance trade-off)
00:48:47 Generative models and GPT-3
00:51:57 Bias/variance trade off -- when do priors hurt us
00:54:48 Capsule networks
01:03:09 Which old ideas whould we revive
01:04:36 Hardware lottery paper
01:07:50 Greatness can't be planned (Kenneth Stanley reference)
01:09:10 A new sort of peer review and originality
01:11:57 Quantum Computing
01:14:25 Quantum deformed neural networks paper
01:21:57 Probabalistic numeric convolutional neural networks
01:26:35 Matrix exponential
01:28:44 Other ideas from physics i.e. chaos, holography, renormalisation
01:34:25 Reddit
01:37:19 Open review system in ML
01:41:43 Outro
Released:
Jan 3, 2021
Format:
Podcast episode
Titles in the series (100)
Kernels! by Machine Learning Street Talk (MLST)