Lets imagine you are looking at library of books and you want to see which books are longer overdue than expected (with characteristics such as word count, genre, author), one plausible method would be to split all books into two unique sets. The first is used to train the model and the second is used to predict the expected time overdue. And then these sets are swapped around and the other side’s over or underdueness is predicted. Does this method have a name? And is there any other method available to do something similar. (Another example would be to predict under and over pay based on employee characteristics_)

Thanks.

Disclaimer: This is not my project. I found this on ycombinator and thought it was interesting.

Link here: http://www.aidungeon.io/

The game can be run in Google Collab so no need to set up any environment.

This is a pretty neat application of GPT-2. I’ve messed with the small version of GPT-2 and you can tell the difference that the 1.5B params gives it. For storytelling, it sorta works and sorta doesn’t. Continuity is pretty flakey, but that gives it a very dream-like feel, where the world molds itself around the actions you are taking.

It’s a long ways off, but if this gets much better, I could see some people getting lost in the generated worlds. Kinda like the librarians in the library of Babel.

Paper: https://arxiv.org/pdf/1912.02781.pdf

Code: https://github.com/google-research/augmix

We propose AugMix, a data processing technique that mixes augmented images and enforces consistent embeddings of the augmented images, which results in increased robustness and improved uncertainty calibration. AugMix does not require tuning to work correctly, as with random cropping or CutOut, and thus enables plug-and-play data augmentation. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance by more than half in some cases. With AugMix, we obtain state-of-the-art on ImageNet-C, ImageNet-P and in uncertainty estimation when the train and test distribution do not match.

The gist of what I’m trying to achieve is, I want to give it 3 similar articles about the same topic and I want it to generate an article on the same topic but different.

I’m not the best at coding and only have a surface level knowledge so I’m at a lost on how to actually use GPT-2.

My first goal was to generate an environment using anaconda, then use python and while that did work, I failed to actually generate a text or even make GPT-2 work.

I’m missing a lot of key steps and would love someone to point me in the right direction!

See the OpenAI blog post and their paper.

Contrary to conventional wisdom, we find that the performance of CNNs, ResNets, and transformers is non-monotonic: it first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction.

I’m looking for user data for my Computer Science Masters project “Using Community Detection to Improve Music Recommendations”.

I’ll be using machine learning to examine user music data from Spotify with the aim of improving the songs people are recommended.

I’ve produced a web app where you can consent to data being (anonymously) sampled from your Spotify account. It only takes about 1 minute to log in and would really help me out.

This can be found at: https://james-atkin-spotify-project.herokuapp.com/

Thanks!

Most networks in tutorials only show examples with 10-1000 output classes. What techniques can be used to build and train networks with a million or more output classes?

It seems to me that using the traditional approach would be horribly computationally expensive. Intuitively, I would expect that some kind of hierarchical approach should be taken. Any links to blogs or papers that show these techniques would be appreciated.