aseg

5 ms·

1.
by aseg 8mo ago
Gordo and Bruce are pioneers in the gliding world. One of their coolest flights that shows their creative flight planning shows up in their 3000km flight in the Sierra Nevada's, and the build up to it. Some basics: The major challenge
2.
by aseg 11mo ago
Slightly meta-level: I'm glad the authors finds the ICLR reviews useful, and this illustrates one of the successes of ICLR's policy of always open sourcing the reviews (regardless of whether the paper is accepted or rejected). The
3.
by aseg 11mo ago
The force at the attachment point is constantly changing and depends on several factors. - the weight of either airplane. - the performance of the engine on that particular day (varies by altitude / airspeed / temp / mixture
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by aseg 11mo ago
In gliding, tow upsets are pretty common and, in rare cases, can be fatal. An out-of-position glider out can _easily_ and very quickly overcome the tow planes elevator authority (ability to pitch up or down) which leads to accidents like th
5.
by aseg 1y ago
This is my research area. I just finished reviewing six NeurIPS papers (myself, no LLM involved) on LLM Agents for discovery and generation and I'm finding that evaluating LLM agents on raw performance for a task isn't as insightf
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by aseg 1y ago
Assume you have data for Hooke's law (a spreadsheet with F, x, and other variables) and you want AlphaEvolve to give you the equation ``F = -C_1*x``. Let's say the model hallucinates in two directions: 1. "There is a trigonom
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by aseg 1y ago
Happy to answer them! 1. Because we only have blackbox access to the LLM and the evaluation function might not be differentiable. 2. We're trying to search over the space of all programs in a programming language. To cover enough of th
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by aseg 1y ago
Finally—something directly relevant to my research ( https://trishullab.github.io/lasr-web/ ). Below are my take‑aways from the blog post, plus a little “reading between the lines.” - One lesson DeepMind drew from AlphaC
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by aseg 1y ago
Location: Pasadena, CA Remote: No/Yes Willing to relocate: Yes Technologies: Computer Vision, Code generation, Program Synthesis, Coq, Lean, PyTorch, Julia Résumé/CV: https://atharvas.net/cv/ Ema
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by aseg 2y ago
If you're looking to run a .exe file, there are a couple of hypervisors in the market (sometimes found on the high seas). I've tried these on a couple of obscure .exe files: - Parallels - VMWare fusion - Apple's gamekit Paral
11.
LeanDojo: Theorem Proving in Lean Using LLMs(leandojo.org)
170 points by aseg 2y ago|53 comments
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LeanDojo: Theorem Proving in Lean Using LLMs(leandojo.org)
2 points by aseg 3y ago|0 comments
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Gradient Hacking(lesswrong.com)
2 points by aseg 4y ago|0 comments
14.
by aseg 4y ago
You might find (Cramer, 1985) interesting! IIRC they go into exactly this problem. However, I can't find an open pdf to link to unfortunately. I'll edit this list tomorrow morning in case I misjudged (Cramer, 1985) or if I find a
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by aseg 4y ago
I encourage everyone to read this paper. It's well written and easy to follow along. To the uninitiated, SR is the problem of finding a mathematical (symbolic) expression that most accurately describes a dataset of input-output example
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by aseg 4y ago
The algorithms are generally in the area of machine learning + programming languages and pretty flexible. This paper talks about how we "bias" these algorithms for applications in the hard sciences (specifically talking about beha
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by aseg 4y ago
Hello! I'm one of the authors on this. Feel free to ask me any questions. Happy to clear up any confusion about what we're talking about as well! Here's a survey paper describing the algorithms used under the hood: https:&#x