https://github.com/lmcinnes/umap_paper_notebooks/blob/master...
EDIT: I should add, unless it isn't clear, that we really should retire the dataset. Something like the QuickDraw dataset makes a lot more sense to me.
I will add a comment/edit to the post once I am home to clarify the relative ease of solving MNIST
I think it is indeed interesting as it shows that Gzip can capture the same things that e.g. UMAP et co. are capturing when they acheive good scores on MNIST.
Also, I'll add, that even despite some of the suspicions people have cast on the Gzip results in other experiments, I'm bullish on the utility of reducing entropy for classification problems.
From a research perspective, MNIST is basically solved. I think current performance on MNIST is better than humans, correct?
But it's still valuable as a teaching tool or a "Hello world" data set, because it's so simple and because most reasonable algorithms will reach 97% accuracy. Even if you build your tools from scratch, it makes a nice homework-sized problem. And it's an obviously useful task that anyone can understand. "Oh, digit recognition for mail!"
> UMAP, which stands for Uniform Manifold Approximation and Projection, is a dimensionality reduction technique and data visualization method commonly used in machine learning and data analysis.
My two cents is that if your problem can be solved with something like UMAP + kNN[2], then you really shouldn't be using Deep Learning to solve it.
[0] https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduc...
[1] https://en.wikipedia.org/wiki/T-distributed_stochastic_neigh...
[2] https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
I'm not sure where I sit on that, but regardless, my understanding is that getting UMAP to separate MNIST doesnt really require you to turn knobs.
[0] https://www.biorxiv.org/content/10.1101/2019.12.19.877522v1
I sometimes see HN comments complaining that acronyms and terms are dropped with no explanation. These comments come across as entitled to me — instead of complaining, why not be curious and ask “what does that mean in this context?”
Imagine someone dropping in here and complaining, “the article sucks because it uses the word ‘compiler’ and assumes we all know what a ‘compiler’ is.” This is what it sounds like to those of us to work in specialized fields.
Instead if someone said, “I’m new to this, could someone help me understand what a ‘compiler’ is?”, it demonstrates curiosity and people are more inclined to explain.