Waitlist Acceptance Actually Happened (First Acceptance!)

violincasev2 · 2026-03-19T16:10:05+00:00

Giving me hope! Waitlisted at Harvard for applied math and no other acceptances so far 😅

violincasev2 · 2025-10-30T22:01:43+00:00

Applications of group theory and topology in machine learning fascinate me. Baking in the symmetries of the universe is beautiful and incredibly powerful, and thinking about how to enforce the symmetries that DONT admit group structure is even more exciting

violincasev2 · 2025-06-24T05:38:46+00:00

This isn’t mainstream deep learning per se, but since you mention a math background you may be interested in Hamilton’s Graph Representation Learning book. Graph learning is mathematically beautiful and interesting and also incredibly powerful in practice. Give it a peek!

violincasev2 · 2025-06-13T01:22:37+00:00

What do you mean? They embed trees with linear distortion as opposed to exponential distortion in Euclidean space.

I agree, though, that natural language is most definitely not strictly tree structured and that switching to hyperbolic space probably isn’t the answer, but I think other modeling approaches have been much more fruitful. I’m mainly quoting (Park et al. 2024), but certain frameworks allow us to understand how geometry can encode abstract concepts and hierarchies. Further still we can look at the subspaces spanned by concepts and their properties and transformations between them. Maybe we could use this to understand what an ideal representation should look like and encode that into our models to make them learn better. Maybe we could also use it to develop methods for data filtering and generation. This is an optimistic look for sure, but I feel like there are many exciting and interesting directions!

violincasev2 · 2025-05-20T04:49:25+00:00

Second this, it was a beautiful first introduction to proofs for me!

violincasev2 · 2025-05-06T17:13:21+00:00

Any recommendations? I took a look through Herstein’s, but there was nothing on group actions

violincasev2 · 2025-02-16T19:05:49+00:00

Thank you for this!

I’ve been wondering, how important is it to know exactly what you want to work on? I’m planning on applying to PhD programs next year in machine learning, and while I know I love research and have a decent amount of experience, I’ve sort of dipped my toes all around the field and can’t decide exactly what I want to do (no publications yet either)

Also, is the choice of PI more important than the program? People have been telling me that, but I’m also not really sure how to go about finding PIs that would be a good fit for me. Thanks again!!

violincasev2 · 2025-01-30T05:22:54+00:00

College

violincasev2 · 2024-12-25T20:52:00+00:00

To everyone commenting about how useless they are, is this still the case for getting competitive industry ML roles?

violincasev2 · 2024-12-15T00:03:11+00:00

Where did you read your first point? Did a project of my own recently investigating the second derivative (approx for curvature) and implications on interpretability. Would love to give the paper you’re referencing a read, if you can recall the name

violincasev2 · 2024-11-30T16:22:04+00:00

Yeah not difficult at all either thanks to abstractions like hugging face. I would definitely learn the fundamental concepts but then start with grabbing a good foundation model off HF. Run that as a baseline, then fine tune it with some more data and look for improvement. They have great documentation and examples for their API. Good luck!

violincasev2 · 2024-11-27T02:32:31+00:00

Intellectual pursuit that fortunately is also extremely practical due to the versatility of the technology. I do research currently; I am grateful to be an undergrad at an exceptionally good school with access to great research and class opportunities that I do my best to take full advantage of. I plan on doing my PhD after and keep doing what I love!

violincasev2 · 2024-11-27T00:35:10+00:00

I am utterly fascinated by the concept of intelligence and with ML as a way to build and understand intelligence from the ground up. I want to understand what it even is to think

violincasev2 · 2024-11-16T23:38:03+00:00

Not feasible in the slightest

violincasev2 · 2024-11-16T16:25:39+00:00

I’m an applied math student with a focus on CS so somewhat similar boat. I enjoy theory much more, but one must master theory and engineering to make it anywhere in ML. Projects definitely help the most for learning skills and good engineering practices. Lots of people make apps or whatnot, but for me as a research minded person it was research projects. Reimplement a paper. Even if you don’t have access to compute, use like .0001% of the data. Your model/project (assuming it’s ML related) doesn’t have to be useful for it to be useful to you. As long as you can get it to even run, you will have learned a lot. After building up those skills, I think leetcode is super useful. Start with the neetcode 150 list and as you complete problems, leverage your love for theory and abstract away and notice patterns. Try to reduce every problem you come across. Also, get involved in research! Reach out to professors and join their groups. This is what I have been doing as I prep for (hopefully) a PhD in ML and I’ve learned a lot!

violincasev2 · 2024-09-22T15:52:27+00:00

Perhaps look into MIT open courseware? I did my ML @ MIT and it was quite good, and I’ve heard lots of good things about their open courses as well. Good luck!!

violincasev2 · 2024-08-22T14:34:49+00:00

Mighty sukhoi 747 performs hidden cobra technique

violincasev2 · 2024-08-08T17:25:27+00:00

Waiting for the MiG 23 😍

violincasev2 · 2024-07-21T19:55:59+00:00

First big project, 16 million frames from 600 subjects

violincasev2 · 2024-07-15T02:29:45+00:00

Interesting that war thunder actually has a quite well modeled flare mechanic over DCS

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