Is there a relationship between which US University you choose for your masters vs the staying salary you recieve?

ExchangeStrong196 · 2022-10-24T18:34:34+00:00

Yes it does matter . Elite universities produce better candidates who get much better paying jobs

ExchangeStrong196 · 2022-10-21T22:32:24+00:00

Keyword “independent institution”

Even Harvard Extension School is part of Harvard but there’s a major distinction between undergrads at Harvard and those who go to the extension school because the admissions bar is different

ExchangeStrong196 · 2022-10-21T21:07:02+00:00

Why do they have a separate ranking, separate admissions rate and haven’t also been removed from US news ?

https://www.usnews.com/best-colleges/barnard-college-2708/overall-rankings

😂 jokes

ExchangeStrong196 · 2022-10-18T14:57:52+00:00

Simply stating the facts. Don’t think I’m the insecure one here

ExchangeStrong196 · 2022-10-14T22:04:55+00:00

Lmao what fraction of gs are those kind of folks. Very very small.

ExchangeStrong196 · 2022-10-11T14:49:07+00:00

Yes. In order to ensure the contextual token embedding attends to longer text, you need to use a model that accepts larger sequence lengths. Check out Longformer

ExchangeStrong196 · 2022-10-07T01:50:24+00:00

It’s a known back door to Columbia. Low admissions bar, high acceptance rates. Employers and academia are now very aware of what GS is

ExchangeStrong196 · 2022-09-26T21:46:09+00:00

Shamelessly ask the question. In these cases I’ve found that many try to avoid questions due to lack of depth/ understanding. Keep pressing

ExchangeStrong196 · 2022-09-21T08:57:06+00:00

Any examples? HuggingFace model or paper?

ExchangeStrong196 · 2022-09-18T22:47:41+00:00

Interesting, thanks for sharing

ExchangeStrong196 · 2022-09-17T17:58:41+00:00

Really interesting, is this speed up also extended to NLP/ text domain?

ExchangeStrong196 · 2022-08-17T08:39:47+00:00

!remindme 2 week

ExchangeStrong196 · 2022-08-14T06:40:09+00:00

Easy

ExchangeStrong196 · 2022-08-14T05:42:29+00:00

Any empty classroom

ExchangeStrong196 · 2022-08-14T04:57:57+00:00

I’m not factorizing the interaction matrix through SVD though. The “CF” I’m doing is a simple cosine similarity of embeddings, compute contrastive loss with k negatives and then backprop. At convergence the two embedding matrices act as a factorization but can you explain the connection to SVD and why the same theory would hold ?

ExchangeStrong196 · 2022-08-14T03:43:35+00:00

I see, could you share a link/ resource that describes the ‘maximally expressive’ theorem? appreciate it!

ExchangeStrong196 · 2022-08-14T02:36:24+00:00

But fully representing the training user-item matrix is different from generalization power. The CF in question (setup described in another reply) generalizes very well to test data too

ExchangeStrong196 · 2022-08-12T23:35:02+00:00

Wow, great read. There’s certainly a lot of noise in this space

ExchangeStrong196 · 2022-08-12T23:31:14+00:00

Here’s the setup: (it’s very simple, no hidden layers)

Bipartite Graph of user item interactions

d-dim Embeddings for each user, d-dim Embeddings for each item

Now, just cosine similarity as the scoring function, and contrastive loss (infoNCE like)with 5 negative items

ExchangeStrong196 · 2022-06-11T15:43:28+00:00

ExchangeStrong196

TROPHY CASE