Viewing profile — lmcinnes

> alignment tooling is fascinating, as we increasingly want to re-fit->embed over time as our envs change and compare, eg, day-over-day analysis. This area is not well-defined yet …

> And algorithms can only predict content that you've seen before. It'll never surprise you with something different. It keeps you in a little bubble. This is not true at all, algo…

Assuming you have a dimension-reduction or manifold learning tool of choice (UMAP,PacMAP,t-SNE,PyMDE,etc.) then DataMapPlot ( https://datamapplot.readthedocs.io/en/latest/ ) is a l…

If you just want in-memory then PyNNDescent ( https://github.com/lmcinnes/pynndescent ) can work pretty well. It should install easily with pip, works well at the scales you mentio…

This is a data map providing a view of all Hacker News stories with a score of at least 2 (to remove most of the spam). Stories are close together in the map if they have semantica…

This is a data map providing a view of all HackerNews stories with a score of at least 2 (to remove most of the spam). Stories are close together in the map if they have semantical…

Zoom in for more detailed topic area labels, hover for paper titles, click on a dot to search for the title of the paper. Search works over paper titles.

For suitable specialized cases thins can be quite efficient. For persistent H_0 of VR-complexes in low-dimensional space there is an O(N log(N)) algorithm for N data points; that's…

It is really not that much slower for training (see the paper), and if you are interested in pipelines the difference is not so great considering you are looking at a one off train…

If this is a thing you want to be able to do efficiently then ParametricUMAP (see [docs]( https://umap-learn.readthedocs.io/en/latest/parametric_umap.... ) and [the paper]( https:/…

Density based clustering with high dimensional data will tend to struggle. This is because, in high enough dimensions, you need a lot of samples to see any density. Also distances …

I suspect that this is because GPT-2 doesn't have any overarching narrative that it is piecing together. Ultimately it is like a super-powerful Markov based text generator -- predi…