Comments (2)
We do train the regressor on the noisy data, as stated in the paper:
"Our method uses a regressor gη which is trained to predict target properties y of a clean graph G from a noisy version of G: gη(Gt) ≈ y(G)."
We are very happy of your interest in our work, but please double check the paper before opening Github issues :)
from digress.
Edit: I see the noising is done in Qm9RegressorDiscrete
now. Sorry!
from digress.
Related Issues (20)
- About experiment on Comm20. HOT 2
- Checkpoints for other datasets
- About node feature in non-molecular datasets HOT 1
- The planar model released performed better than described in the paper
- There occurs nan when computing test/X_logp, test/E_logp, and test/y_logp with "SumExceptBatchMetric" in abstract_metrics.py
- Your work in this paper is of great significance and it is my honor to present it in our group meeting. For my better understanding and presentation about your work, would you mind sharing me your presentation slides and viedo about this paper? I would very much appreciate it if you shared your slides to me, thanks!
- Something wrong with utils.EMA HOT 2
- I would very much appreciate it if you shared
- I will appreciate it if you could send me a mail HOT 2
- Reporting KL divergence loss for training step
- Question about the architecture (graphTransformer) HOT 2
- Generate graphs by text HOT 2
- bug found for Error: datamodule has no len()
- bug found for guidance branch. RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)
- problems with loading the checkpoints for planar.ckpt?
- Get the prev sample like in diffusers API
- Could the project use a graph structure to describe interatomic distance information?
- How much memory is required for data preprocessing? HOT 1
- Need some help of conditional generation
- Normalization for congress
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from digress.