This explanation is amazing... it asks for an understanding of basic concepts of linear algebra and and statistics, but it is still clear enough to understand it, when knowledge in these subjects is more based on intuition than on in depth education Thanks a lot for this, it's really great!
wow... this is absolutely brilliant. Due to the bijective nature of the normalizing flow, you're constrained to only utilizing bijective functions (which is quite limiting indeed). However, by designing the NN structure in this way, you're able to offload parameter learning to an entire internalized NN, where the NN outputs parameters for a bijective function. Mind blown, crazy stuff! After all this is complete the learning piece simply being MLE makes a ton of sense. Dank je wel!
Thank you so much for such a great, clear and easy to follow explication, I like the comparison between flow-based models, GANs and VAEs at the end of the video ! Also, the math explanation is very clear :)
Hello, thanks for your explanation. I don't understand how shuffling is an invertible function, do you have to remember the places where you shuffled your points?
I think each random shuffling is selected once when the architecture of the network is established, and does not change after that. Done this way, the shuffling can be undone.
Thank you so much for this video. I'd watched several videos on flow before this one, but this is where it really clicked for me. I echo @MathStuff1234, absolutely brilliant.
This explanation is amazing... it asks for an understanding of basic concepts of linear algebra and and statistics, but it is still clear enough to understand it, when knowledge in these subjects is more based on intuition than on in depth education
Thanks a lot for this, it's really great!
This is the greatest explanation of coupling layers I've seen. Thank you
it like diffusion model now aday! greate!
wow... this is absolutely brilliant. Due to the bijective nature of the normalizing flow, you're constrained to only utilizing bijective functions (which is quite limiting indeed). However, by designing the NN structure in this way, you're able to offload parameter learning to an entire internalized NN, where the NN outputs parameters for a bijective function. Mind blown, crazy stuff!
After all this is complete the learning piece simply being MLE makes a ton of sense.
Dank je wel!
I think at 9:00, because of the chain rule we must evaluate not at x, but like this (example for 2 functions): Df(x)=Df_1(f_2(x)) Df_2(x)
Hi, that is an amazing lecture. Thank you so much for the video. Could you please post the lecture powerpoints?
Nice video again, however I could not wrap my head around: If I use random shuffling, how can it still be invertible?
Great explanation! Straight to the point and clear!
This is the most useful lecture for starting normalizing flow!!!
This is an incredibly nice explanation! Thank you so much
Are diffusion models are specific implementation of it? Or something else?
Thanks for your generative model series!
Thank you so much for such a great, clear and easy to follow explication, I like the comparison between flow-based models, GANs and VAEs at the end of the video ! Also, the math explanation is very clear :)
Hello, thanks for your explanation.
I don't understand how shuffling is an invertible function, do you have to remember the places where you shuffled your points?
I think each random shuffling is selected once when the architecture of the network is established, and does not change after that.
Done this way, the shuffling can be undone.
Same here. This does not look differentiable
Hello, I really enjoyed the explanation. It was easy to follow and the analogy was very useful!
The explanations! very impressive.
Thank you so much for this video. I'd watched several videos on flow before this one, but this is where it really clicked for me. I echo @MathStuff1234, absolutely brilliant.
very nice explanation for me as an data science studen thank you
Extremely good!