So do these odes model a function as simply a function of time, because I want to make it as a function of time with position and velocity as initial condition parameters. How do I do this?
well it's ODEs of the form x' = f(x, t) so f is modelling the velocity - the only inputs you can put in are positions and time. To input velocity you'd need f to give you: f(initial_position) = initial_velocity. This is a little tricky. You could include include training examples [initial_position, initial_velocity] and train f on this with a big penalty for deviation
Exciting work! I look forward to to being able to eventually understand it haha
Wow, at 9:59 we see the very beginning of diffusion models! Very cool to see something so impactful in its infancy
This video is amazing. Researching this very paper since last week and seeing the actual presentation is lifesaving
Amazing work!
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Amazing talk, and a huge dislike for the amount of commercials you put in this video. shame
So 1/2 the parameters, but 2-4x the FLOPs?
You can say so but It depends on the actual use case
So do these odes model a function as simply a function of time, because I want to make it as a function of time with position and velocity as initial condition parameters. How do I do this?
well it's ODEs of the form x' = f(x, t) so f is modelling the velocity - the only inputs you can put in are positions and time. To input velocity you'd need f to give you: f(initial_position) = initial_velocity. This is a little tricky. You could include include training examples [initial_position, initial_velocity] and train f on this with a big penalty for deviation
2:26
What is theta please ? I don't understood.
Theta is the parameters
@@youcancode6988 What parameter for example ? And theta seems to be a function of time
@@youcancode6988
I hope you don't comment your code by writing "var1 is the first parameter, var2 is the second parameter, [...]"
@@OneShot_cest_mieux Theta is the weighted values given to each input on each node in each layer of the neural network
@@youcancode6988 ok thank you