Wow! So many cool observations and super clever tricks! Plus Bruno is very good at explaining enough of the background succinctly so that it is easy to follow. This ability he has to make things intuitive makes a huge difference for me. The lecture is sufficiently self-contained so that you don't go off the rails because of some small thing you don't know and then the rest of the lecture would be incomprehensible. Kudos!
Such a wonderful talk! Thank you for sharing! This talk helped me understand a puzzle I have had for a long time: how is human visual perception more efficient than machine learning, expressed in math? It makes sense to decompose the mechanism into some key factors, such as equivariance and invariance, and use combinatorian to simulate large numbers of possibilities. Bruno has done a great job explaining math models in such an intuitive way that I can understand the basic ideas without getting into too many technical details. My original naive idea was that learning in motions must have played a substantial role in human vision, so maybe we should use videos instead of static pictures in machine learning. But then it would further worsen the problem of computational powers. But these sets of research seem to open a new promising path! Looking forward to more exciting findings!
52:05 Main points - 1. Animal behavior tells us what problems the brain is solving, 2. Biological structure gives us clues about the mechanisms involved, 3. Mathematical structure provides the computational foundations
Don't let anyone tell you; your elaborately pontificated assertion is anti-egregious. Someday soon, I hope to cogitate scientific nomenclature such as Bruv. However lamentably, I too use the penultimate, pejorative; Bruh.
One of the best lectures i've ever seen
Wow! So many cool observations and super clever tricks! Plus Bruno is very good at explaining enough of the background succinctly so that it is easy to follow. This ability he has to make things intuitive makes a huge difference for me. The lecture is sufficiently self-contained so that you don't go off the rails because of some small thing you don't know and then the rest of the lecture would be incomprehensible. Kudos!
Such a wonderful talk! Thank you for sharing! This talk helped me understand a puzzle I have had for a long time: how is human visual perception more efficient than machine learning, expressed in math? It makes sense to decompose the mechanism into some key factors, such as equivariance and invariance, and use combinatorian to simulate large numbers of possibilities. Bruno has done a great job explaining math models in such an intuitive way that I can understand the basic ideas without getting into too many technical details. My original naive idea was that learning in motions must have played a substantial role in human vision, so maybe we should use videos instead of static pictures in machine learning. But then it would further worsen the problem of computational powers. But these sets of research seem to open a new promising path! Looking forward to more exciting findings!
Great presentation! It brought me many inspirations for my Graph neural network research.
52:05 Main points - 1. Animal behavior tells us what problems the brain is solving, 2. Biological structure gives us clues about the mechanisms involved, 3. Mathematical structure provides the computational foundations
Theoretical Neuroscience sounds like a lovely field, is it popular in Europe?
Amazing video, thank you for publishing
very interesting presentation... hope some day i can contribute to research like this..
Amazing content
Thank you. 😊
Bruh, amazing stuff
Don't let anyone tell you; your elaborately pontificated assertion is anti-egregious. Someday soon, I hope to cogitate scientific nomenclature such as Bruv. However lamentably, I too use the penultimate, pejorative; Bruh.