CM
@Creative_Math_
Master’s student @UofT 🇨🇦. Did pure math in a past life, now I’m trynna learn how to trick computers into thinking. cashmere-y
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Alright, I’m about to go deep into the RL for LLMs trenches for some days, will be back with an implementation of a paper that I’ll share (if things go well)
This is what it feels like to finally prove the result your team was working on for a month only to realize there's a fatal flaw in the argument as you're explaining it to them
But getting dressed in the morning, to which our civilization is now devoting so many of its marginal resources, is possessed of neither beauty nor symmetry. Indeed, putting one’s socks on before one’s shoes is quite different from putting on one’s shoes before one’s socks.
The reason for this is simple: Math is not a spectator sport. There is no free lunch and you learn by thinking through it yourself I always try to work something out myself from a video of his after watching it, this has worked well so far
I love 3Blue1Brown. I will rewatch any of those videos infinity times. I don't think I've ever learned/retained anything from one though, and I'm suspicious of people who point to it as a superior learning resource. (Not a knock on 3B1B; truly an amazing achievement).
They used an interesting math result here though: If f : R^n -> R is analytic, its zero set is measure 0 This ends up being non-standard (there’s a 4 page arXiv preprint with >200 citations proving it) but I wrote a nice and simple proof that fits in one screenshot
Pretty much every piece of coverage on this paper I've seen is a misrepresentation of what it says. For starters, this is a measure-theoretic statement. The probability of something being 0 does NOT mean it never happens. The main result uses a theorem on analytic functions so-
For the TTS we used an audio model by @boson_ai called HiggsAudiov2 (github.com/boson-ai/higgs…) They can also generate audio, the crazy thing was how good their generative model is at voice-cloning with just a sample SENTENCE Time to understand how audio generative models work
Ig I’ll post the demo We built a voice-controlled photoshop that lets you edit imgs in a project. It parses intent to choose bw a native transform or an in-paint GenAI edit (which maintains image consistency) Segmentation happens with SAM, generative edit by Qwen-Image-Edit
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