Where is the Knowledge Alignment Network for the Energy Conservation?
zengrh3 opened this issue · 7 comments
Where is the Knowledge Alignment Network for the Energy Conservation?
Thank you for your interest in our work. The law of conservation of energy is not directly applicable to SEVIR. The code for experiments on N-body MNIST will be released in the near future.
Thank you. I wonder whether you can just release the code in terms of the law of conservation of energy training. I would like to refer to your code to make some adjustments to my task.
I have noticed in Table 9 in the Appendix. It seems that the design of the knowledge control networks is almost the same for the
Also, I have noticed that in you set the cond_ids
to [0, num_timesteps - 1, num_timesteps - 1, ..., num_timesteps - 1] in the following function:
So this means in the you will probably always set the
tc
to num_timesteps - 1
in most cases:Am i understand correctly?
Also, I have noticed that in you set the
cond_ids
to [0, num_timesteps - 1, num_timesteps - 1, ..., num_timesteps - 1] in the following function:So this means in the you will probably always set the
tc
tonum_timesteps - 1
in most cases:
Am i understand correctly?
Oh, I saw the difference.
For the zc = self.q_sample(x_start=zc, t=tc, noise=torch.randn_like(c.float()))
, the timestamp will be almost the num_timesteps - 1
for everywhere. But for the zt = self.q_sample(x_start=z, t=t, noise=torch.randn_like(z))
, the timestamp will be sampled randomly.
One more question, I noticed that in the below
The
zc
and the y
are as input to the self.torch_nn_module
, which i think should be the input to the below forward function of NoisyCuboidTransformerEncoder
classHowever, I could not find the exact usage of
t
and zc
in the implementation of forward function of NoisyCuboidTransformerEncoder
class.
Please point me out if I miss somewhere. Thank you.
Just want a clarification: in equation (4) in Prediff paper, the input to the constraint function is
I just wonder why Prediff designed to use
Thank you very much.