I have a task and I don’t know how to tackle this. I received a set of positives and I have to find similar points from a big dataset (that I call basket). I have around 1’000 positives and around 1’000’000 points in the basket. All points are represented with 10 to 15 features. As an output, I would like to have a score for each point of the basket and this score would represent the closeness of the point to the positive set.

I first thought of using a k-nearest neighbours method on the positives but this approach presents two big drawbacks for me. First, I wouldn’t have a score associated to each point of the basket as I would only have a set of close points for each positive. Secondly, and this is the biggest drawback in my opinion, I would have to define the distance in the n-dimensional space myself while I would prefer that the method directly defines weights for each feature on the data (for instance, based on the level of information (variance) contained in each feature).

Does someone could point out to me a good approach to tackle this problem?

Thanks!

Hi,

I’ve been recently away from the research landscape, so I’m not up to date on what’s going on lately.

I know in 2017 the big hype was still Supervised Learning with bigger and bigger models.

Is there anything that might predict upcoming breakthrough in unsupervised ML or simpler supervised models (few-shot/one-shot learning) ?

What do you thing ML will look like in 5-10 years ?

I’m working on a paper and am currently conducting a literature review. I’m building up a bibliography file and I would like if there were an AI that could read the bibliography then recommend papers to add to that bibliography based on those it saw. I could easily envision websites such as Web of Science having such a system (they do not) so I’m just wondering if there is such a site.

I’m involved in facial recognition and was wondering if anyone has tried runwayML?

Any thoughts? what’s your experience been like? would love some honest review here (good/great/bad/ugly)

What tasks/application do you use runwayml for? would love to hear from all

Hi,

I am currently working on a research project that involves using a transformer-like model for a NLP task. Specifically summarizing long documents.

I was wondering if any of you knows of a paper that explores the limits of the transformer when using super long sequences.

Is there any issue with long sequences?

Is there a “length” limit that this kind of model starts to decrease their performance?

Thanks a lot in advance!

Hi all, I’m a machine learning engineer at Duolingo, and I wanted to share a project I’ve been working on called CEFR Checker. It aligns text from many languages to a common language learning proficiency standard (the CEFR) using an ordinal regression model trained over multilingual word embeddings and corpus word frequency estimates. We use this tool when we create Duolingo Podcasts and interactive Stories to help adapt the material to the appropriate CEFR level for beginner, intermediate or advanced learners.

We’re sharing it publicly in case others find it useful as well.

The tool is completely free to use and you can check it out here.

Here’s a blog post I wrote about the project if you’d like more info.