Hi @IntelliSensing 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance), you can also claim the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
I noticed in your abstract that you are planning to release the code for Graph-RHO. Would you also be interested in hosting the pre-trained heterogeneous graph network checkpoints on https://huggingface.co/models?
Hosting on Hugging Face will give your work more visibility and enable better discoverability through metadata tags (like graph-ml). We can link the checkpoints directly to the paper page so that researchers in the combinatorial optimization and GNN communities can easily find and use your models.
If you're interested, I'm leaving a guide here. For graph-based models, you can also easily use the hf_hub_download utility to let people pull your weights with a single line of code.
Let me know if you're interested or need any guidance!
Kind regards,
Niels
Hi @IntelliSensing 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance), you can also claim the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
I noticed in your abstract that you are planning to release the code for Graph-RHO. Would you also be interested in hosting the pre-trained heterogeneous graph network checkpoints on https://huggingface.co/models?
Hosting on Hugging Face will give your work more visibility and enable better discoverability through metadata tags (like
graph-ml). We can link the checkpoints directly to the paper page so that researchers in the combinatorial optimization and GNN communities can easily find and use your models.If you're interested, I'm leaving a guide here. For graph-based models, you can also easily use the hf_hub_download utility to let people pull your weights with a single line of code.
Let me know if you're interested or need any guidance!
Kind regards,
Niels