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Modify autosklearn to accommodate PyOD models and verify their successful execution in the context of autosklearn.
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Hi, first of all, thank you for your help many times before. I don't mean to be rude, but I tested your published model 'MS_SVCONV_B2cm_X2_3head.pt 'at different sample points (5000, 2500, 1000, 500, …
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Issue to capture discussion on workflows, methods, etc, ... with aim toward clear definitions, naming, scope of this effort. This initial comment just to get started. Discussion will add and respond t…
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Hi,
I'd like to use SBERT model architecture for document similarity and topic modelling tasks. However, my data corpus is fairly specific to domain, and I suspect that SBERT will underperform as …
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Hi
Thank you very much for sharing the code for this amazing work.
I have some naïve questions regarding the choice of design for the reco loss.
1) is there any specific reason that you put [Lin…
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@backtime92 Thanks for updating the code. I have a few questions.
1. Why is this function necessary to make the size of the character box 1.5 times larger by using enlargebox?
https://github.com/b…
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Hi Ozan,
Once again, thanks a lot for the great paper and added value to the **computational pathology** community.
I noticed that you have used TCGA UCEC in your training as in Table F.13 of th…
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It seems dwa only considers the rate of change of loss for each task. without the common scale of gradient, which introduced by GradNorm. So can dwa get comapareble results to GradNorm?
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When I modified the shell file for the Cora dataset, and ran the command: `bash experiments/gcn_exp.sh Cora`. The test results only get around 47~48. And we all know that GCN as a classical model will…
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My experiments would want to benchmark the following:
- supervised-only training without semi-supervision
- semi-supervised training (with labels and without labels)
Is it possible to do only s…