For implementation of Bidirectional language model, Please recommend me a paper/github repo that I can refer to.

There’s a lot of papers about uni-directional LM, but I can not find bi-directional one ..

Thanks 🙂

For building a video game AI what would people view as the ideal platform/tools? For both training and simulation.

The training would include the ability to work with prior replays of the game.

Currently based on my very basic research I’m thinking that using something like Kubeflow to enable people to do the data-science side of things and training inside of Jupyter

For the simulation a custom web-based interface to the game where the user can upload scripts and access previously created training data which will be run automatically.

Thoughts?

I posted this a few months ago and had some great feedback. I’ve put some work into the model and have just released the latest update. It uses a modified version of C4.5 decision trees and a load of other adjustments. Think it is working better now after some changes around the classification process. Appreciate any feedback! The link is

https://incredicat.com

Seems if it’s very lame to be discussed on this?.. I’m really not talking about ‘dream prediction’, but because most of the dreams that we have, are something that we happen to see that happen in daily life, or of course of something back few years ago, or that we hear, we see, we speak about. If at all, our biological brain, was analogically, a sophisticated GAN+GPT2 sort of model, would it be real to hypothetically assume that such model gets activated while we are slept? On such assumption, how could be the generator & discriminator be in such model system combined with text generation?. And is this sort of research for dream simulation, prediction really worth it?

This is a place to share machine learning research papers, journals, and articles that you’re reading this week. If it relates to what you’re researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you’ve read.

Please try to provide some insight from your understanding and please don’t post things which are present in wiki.

Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

Previous weeks :

Most upvoted papers two weeks ago:

/u/sasa1163: https://medium.com/@melissa_89553/an-nlp-analysis-of-the-mueller-testimony-6ff38e9d26f

Besides that, there are no rules, have fun.

For those of you who are always on ArXiv digging through the latest research papers and love sharing them with others, this just might be the position for you.

Job Title: Machine Learning Living Library

Basic Job Description: Read though latest papers, track trends in ML and be go-to consultant for researchers in the organization.

I have no relationship to this company. I found this posting looking for ML positions at non-profit organizations.

I recently began doing professional ML research in medical areas, and some family and friends are excited for me and want to understand what I do. How can I explain it to them without coming off condescending or over-simplistic, if they don’t have even a calculus background. (Reposting b/c I forgot to tag the first time)

Why You Should Document Your Work As a Data Scientist

Setting aside the ever-decreasing length and depth of Medium posts, I’m puzzled why basic topics of “communicate wtf you’re doing” in data science seem novel in 2019. It seems like someone’s ringing this bell once a week.

Is this a problem that merits a patronizing call to action?

Ilya Repin’s quote: “At first the artist paints simply and badly, then complicated and badly, then complicated and well, and only then simply and well. ” I think it is maybe actually for ML papers too…

Share papers that you think are simply and well or complicated and well. To see really bad examples is also interesting.

Thanks!

I’m writing a thesis that heavily focuses on semantic segmentation of biomedical images.

I’m reviewing different segmentation approaches, identifying two main approach branches:

• A sliding window-like approach: a classification network is used over different patches of original image to reconstruct a pixel-by-pixel estimates of the probability maps.
• A full-image approach: like the FCNN and UNET approach, rely on fully convolutional architectures and the upscaling phase is incorporated in the network itself using transposed convolutions.https://arxiv.org/abs/1505.04597

The second approach clearly outperforms the first one. I have a vague hunch on why this happens: my hypothesis is that the transposed-convolution operations, being at their core local operations, force local criteria on the segmentation of close pixels so that pixel contiguity is heavily encouraged in the fully convolutional case.

I do not find this kind of explanation satisfying because of two reasons:

1. I do not have papers or real data to support this: I cannot seem to find any paper on the theme.
2. The sliding-window approach has a built-in form of local consistency as well: if overlapping windows share most of the pixels it’s reasonable to think that – given the network is not totally chaotic and shows enough linearity – the outputs would be similar.

Do anyone have a bit of insight or source on any of this? Any contribution, even brainstorming or unsupported hypothesis (like mine) is well appreciated.