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u/Willwaste63

r/fulbo r/norske r/ProgrammerHumor r/NepaliFood r/reinforcementlearning r/CinemaNepal r/NepalStock r/Devvit r/linuxmemes r/duolingo r/soccercirclejerk r/MLQuestions r/NepalSocial r/Pokhara_University r/GlobalOffensive r/IndianTeenagers r/mapporncirclejerk r/IndiasGotLatent r/Stargate r/NepaliFutballJerks r/memes r/Barca r/technepal r/pytorch r/learnmachinelearning
▲ 1 r/Devvit

HTTP Fetch domain exception pending , how long does review take?

So my Devvit app requested HTTP Fetch access to fantasy.premierleague.com for public Fantasy Premier League standings. It shows as Pending in Developer Settings, and fetches fail with “domain is not allowed.”

How long do domain exception reviews usually take? Also, is there any alternative way to fetch public FPL data while the domain is pending, or do I just need to wait?

u/Willwaste63 — 1 day ago
▲ 5 r/NepalSocial

This gov thinks my silence is my weakness, aja lai kei vayena arko pali exam ko bela batti gayo vaney Singha durbar ma ago lagaidinxu.

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u/Willwaste63 — 3 days ago
▲ 108 r/MLQuestions+1 crossposts

I tried making classes more separable with SWD (Sliced Wasserstein Distance), and got interesting results.

So i've been thinking about,

Can we learn a transformation of the features that makes two classes more distributionally separated while preserving the geometry?

So we Given two class distributions A and B, learn a transformation, T(x) such that

it maximizes SWD, max​SWD(T(A),T(B)).

But only SWD maximization using NN would distort the data hence we add some geometry constrains(claude suggested) and Pipeline looks like this

Raw data ->Learn transformation->Increase SWD between classes with geometric constrains-> Standard ML approach.

It worked fine on Decision trees but was super bad for others algorithms, above is its performance for Breast Cancer using DTs.

>

u/Willwaste63 — 14 days ago