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Hello and thank you for developing the torchgfn package! It's been a fantastic resource, and I appreciate all the work that's gone into it.
I'm exploring the package and am particularly interested …
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I didn't find the evaluation or test code in the repo. How do you test the generative model?
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Hi I just want to use GFlowNet for another protein pocket. Now I have a dataset of SMILES and docking scores, but I'm not very sure about the rest of the preparation process of the dataset. For exampl…
G1NO3 updated
7 months ago
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Currently, the codebase uses helper methods (`tfloat`, `tlong`, `tint`, `tbool`) to convert numbers / lists / arrays into tensors with the corresponding dtype and send them to the right device. These …
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Thank you for your fantastic work!
However, I got a problem reproducing the results in your paper. Based on your released code and the method listed in your paper, I got much inferior results.
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There're several methods in the environments which work with states (e.g. `state2policy`, `get_parents`) and either take state as an input or use internal state of the enrolment `self.state`. This was…
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Hi. Thanks for the great work.
While reading the code, I couldn't find the function that takes class label and generate corresponding image in GFlowNet finetuned posterior model (p(x|c)). I noticed…
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Dear Authors,
I am deveping a model that uses your dataset with docking energies. We realized that the docking energy from the AutoDock Vina can have quite a significant fluctuation depending on th…
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Dear Authors,
First of all, I am very thankful for your repository. I got confused about the correctness of implementation in one part. For `soft_dqn.py`, the variable `valid_v_target_next` is get…
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Unless I'm missing it I don't see anywhere a method for conditional generation. For my purposes it would be
1) load conditions in batches from a dataloader
2) assign each env to a condition
3) enco…