what if you fed all the Joe Rogan lines from every single episode and trained an AI to speak like joe rogan idk I’m not a programmer just throwing out an idea

Hey guys, I just started my Masters Program in AI and our prof threw an assignment at us: Find an IEEE paper and recreate the results… Okay.. I have 0 background in any of this stuff but here goes.

I’ve got one on NLP (with the code, but without the dataset) and I’m trying to scrape twitter data. I researched that there was a python script which allows you to do this, however it requires that you have Twitter Dev Permissions. I made a Dev request, made an App and got Consumer API Keys and Access Token Keys. However, my permissions are set as read and write only. If I want to scrape tweets (with certain #) is read and write enough access for me to export to a CSV file to later use to train the model?

I’ve posted on /r/MLQuestions but haven’t gotten a response there so I’m hoping I’ll have better luck here. Hope someone with more experience can shed some light on the topic.

Thank you!

https://i.imgur.com/OqIQ3ox.jpg

Seriously though, I’m only training 60 fits with n_jobs=-1. Any way to speed this up?

If we can industrialise and monetise independent software creation, the possibilities seem endless. The applications for heuristic progress in machine learning could be huge. Thoughts?

https://www.reddit.com/r/btc/comments/d5qhx6/what_is_emergent_coding/

I’m considering using this next quarter but looking for some honest reviews from those who have already started using.

What do you like about it the most? What do you hate about it/are struggling with? What’s good/bad/ugly about AutoML you have come across?

How do you monitor the progress made in your subfields of interest? I’m still studying and I’m not yet a researcher; my only experience was with Arxiv and I had a couple of problems: and there are a couple but when I delved deeper in Adversarial ML for a project I noticed two problems:

1. There’s no notification system. I’d like to know when a new interesting paper get publish in the field I’m interested in but, at least on Arxiv, there’s no option for that.

2. I’m not sure if that’s an issue with the field I was studying at the time (Adversarial ML) or if it’s a general issues but I happened to read papers of researchers claiming great descoveries for later being discredited by established authors. That was quite disappointing other than a waste of time.

Are those common problem? How do you keep yourself updated?

I found a partial solution in using twitter lists to group famous researchers and look at what they post but it’s not really optimal.

Suppose you are given training instances with soft labels. I.e., your training instances are of the form (x,y,p), where x ins the input, y is the class and p is the probability that x is of class y.

Some classifiers allow you to specify an instance weight for each example in the training set. The idea is that a misprediction for a particular example is penalized proportionality to its weight, so instances with high weight are more important to get right and instances with a low weight are less important.

When examples are of the form (x,y,p), it’s clear that the class probabilities could be used as instance weights. A simple way to do this is to weight the loss for each instance by its probability, as suggested here:

https://stats.stackexchange.com/questions/277435/how-can-i-integrate-confidence-of-class-labels-into-my-classifier

Does anyone know of a paper/book where this simple weighting approach is discussed? I can’t find references on this simple idea.

Hey guys! Here’s a fun tool I hacked together by abusing spatialized adaptive instance normalization. You paint directly with the latent Z into different layers of the network (basically StyleGAN with some model surgery). The results are pretty cool! : link to the video

Have a lot of features planned including a “color picker” that’s just an encoder to encode the latent Z of an arbitrary image into your latent space so you can paint with it. Also going to be exposing sliders for which layers you’re painting on (earlier layers will be the coarse features like facial structure and later layers will be more textural).

Hello Reddit! We successfully attacked 5 state-of-the-art Face ID systems – FaceNet, SphereFace, ArcFace, using synthesized adversarial faces via GAN. The adversarial faces look visually realistic and very similar to the original probes (to a human) while evading face matchers.

Our method is trained on FaceNet but via the attack transferability property, the pretrained model can be used to attack any face recognition system.

I eagerly look forward to your comments!

Paper: https://arxiv.org/abs/1908.05008