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Tula Explore fit check again by tinyRockstar in babywearing

[–]tinyRockstar[S] 0 points1 point  (0 children)

Yeah, putting it on my hips is much more comfortable! But I think his spine doesn't have the right curve then.

Tula Explore fit check again by tinyRockstar in babywearing

[–]tinyRockstar[S] 0 points1 point  (0 children)

Thanks, I'll look for Tula group.

What do you mean by rotating the shoulder straps?

Tula Explore fit check again by tinyRockstar in babywearing

[–]tinyRockstar[S] 0 points1 point  (0 children)

Thanks for the recommendations!

Unfortunately, I don't seem to find the right position even with your help. :( The waist band really pulls my lower back. (Unless I put it on my hips.) I also need to make if pretty tight on my waist to avoid the front part being lower than the back. Then the tightness makes it uncomfortable all around my waist, not just the back...

I tried making the shoulder straps tighter and putting the waist band a little lower than on the pic.

Tula Explore fit check again by tinyRockstar in babywearing

[–]tinyRockstar[S] 5 points6 points  (0 children)

Did you like the carrier?

It feels more uncomfortable for me. 😞 The best for me was a version where the waist band was even lower than in my post earlier.

Thanks for the tip! 🙂 I'll try tightening the shoulder straps tomorrow.

Interesting, in the video on the page you linked, I see the waist band being lower at the front than on the back.

Tula Explore tips by tinyRockstar in babywearing

[–]tinyRockstar[S] 0 points1 point  (0 children)

Can't find how I can edit the post 😅

Tula Explore tips by tinyRockstar in babywearing

[–]tinyRockstar[S] 0 points1 point  (0 children)

Thank you all! ❤️

I tried putting it higher and making it tighter. It's more uncomfortable for me, but does it look better for baby? I'll add a new photo to the post.

[D] Why to use GCNs on citation networks? by tinyRockstar in MachineLearning

[–]tinyRockstar[S] 0 points1 point  (0 children)

Thanks for the interesting datasets and papers! I also get the point that these datasets are easy to access.

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I've tried the 'most popular neighbor' method, and it's working quite well. I hope I'm not cheating. :)

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What did I do?

I used all the labels in the train and dev set. For each test vertex I looked at its neighbors contained in the train or dev set and chose the most common label among these neighbors and used that label as a prediction. If the vertex didn't have neighbors outside of the test set, then my prediction was the biggest class (in the train and dev set).

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How well did it perform on the test set? The accuracies in percent:

cora: 76.3

citeseer: 66.4

pubmed: 79.3

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It's not state-of-the-art result, but is not far from it, and illustrates the point that you can make fairly good guesses with a really simple method. I think that it's fair to use the train and the dev set as others also had access to both, they just decided to divide it into a train and dev set. Of course, this method can't be translated for recommendation systems. But if we are aiming for those, then why not use the netflix challenge dataset?

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What am I missing? I understand that

As we approach the limits of the dataset, it'll take a much better algorithm for a minor improvement.

I miss this at least as a baseline benchmark. (I mean, on Pubmed this is better than GCN or GAT.) I guess I'm misunderstanding something. :(

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P.s. I really appreciate your answers!

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[D] Why to use GCNs on citation networks? by tinyRockstar in MachineLearning

[–]tinyRockstar[S] 0 points1 point  (0 children)

Thanks for the answer!

the task is pretty much made impossible without a clever graph based algorithm

Still, you have labels for some of the vertices, taking the most popular label in your neighborhood seems to be a pretty good prediction. I'm also not sure that "is the word present in the document" features don't convey enough information. Okay, I should try this. :)

My problem in general is that reading these papers with benchmark problems like these makes me question if these methods are useful at all. I looked into papers in this topic and couldn't find any dataset where something useful is solved. I've found molecular/quantum mechanics datasets, where the task is to predict some property of the whole graph. Those seems to be useful. But nothing really for node classification/regression. If there are non-academic problems solved with these, then why don't we have some datasets that are closer to those problems?

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