Could someone give me a run down of what kind of papers should be submitted to each ML conference (I.e. theoretical ML to NeurIPS etc)?

I was attracted away from AWS by paperspace claiming it’s so fast and easy with ML in a box. Well it seems fast and easy but in reality it’s a different kind of painful. Don’t get me wrong getting nvidia drivers and docker to work in AWS is quite the challenge sometimes.

Paperspace offers their own “web” terminal doesn’t seem to support copy/paste, their inbuilt remote desktop connection is very laggy and keeps dropping out and to use my own SSH connection I have to pay a 3 dollar a month fee for a public IP?!?

Honestly what hurts me the most is the incosistent copy paste. It barely works in their native app and their native terminal. It’s unbelievable how buggy their connection software is. On top of that the password they provide when you create a machine doesn’t seem to be the root password. I can sudo just fine but there doesn’t seem to be a way to become root at all. I’m a little in disbelief.

Has anyone had any good experience with their system? I think there’s no way their system is this bad an I’m just using it wrong.

I was toying around with GPT-2 and found it can actually generate some pretty messed up / funny CAH cards! I guess the pretraining data contained some pretty toxic stuff (aka the internet). So I built a little game around it: https://www.aiagainsthumanity.app/

Right now, you can select your favorite answer out of five choices. I log all decisions made in order to train an AI-opponent in the future. Some features that are planned:

• Play against an AI opponent
• Invite your friends
• More cards, also questions with more than one gap
• Share your favorite card combos with friends
• Level up
• Info page for each card where you can discuss and vote on them

Let me know what you think!

Oh and big thanks to u/ablacklama who initially had the idea to create CAH cards using a Char-LSTM.

https://thegradient.pub/over-optimization-of-academic-publishing-metrics/

​

Michael Fire claims that authors in machine learning (and also in general academia) are gaming their paper and citation counts.

Hello r/Machine Learning,

I have a couple of questions regarding entity/categircal embeddings that I hope we could clear out:

1) Can Entity/Categorical embeddings be used for an output layer (for multi-class classification task)?

2) Can I use entity/categorical embeddings if I a variable can store several categories (for example I have three classes {A, B, C} and an object can be {A and B})? Is there a python implementation for it?

3) Is Keras Embedding layer is actually performing entity embedding? The documentation doesn’t seem to be clear in this regard

Thank you 🙂