imurray

6 ms·

1.
by imurray 2mo ago
> It shouldn't be too hard for the gov to commission a RosettaStone like german learning app ((suboptimal, i know)) that works somewhat well and give it out for free, right?) Deutsche Welle (DW) is funded by German taxes and has mat
2.
by imurray 2mo ago
John Skilling has used a generalization of the Hilbert curve to n-dimensions in his BayeSys software for Bayesian inference: https://www.inference.org.uk/bayesys/ -- the manual describes how (pdftex will make a nice pd
3.
by imurray 3mo ago
Here is a pytorch optimizer that can maintain a matrix as orthogonal throughout optimization: https://github.com/adrianjav/pogo — POGO: A Proximal One-step Geometric Orthoptimizer https://arxiv.org/abs&
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by imurray 3mo ago
I think you're thinking of "You Suck at Excel" by Joel Spolsky. https://www.youtube.com/watch?v=JxBg4sMusIg Lots of past HN discussion... https://hn.algolia.com/?q=you+suck+at+excel That vide
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by imurray 3mo ago
The Ubuntu backport did include regressions that an Ubuntu update a couple of days later addresses: https://ubuntu.com/security/notices/USN-8349-2
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by imurray 3mo ago
That version has security fixes from the same day as the latest rsync release: https://ubuntu.com/security/notices/USN-8283-1 As usual, Ubuntu backported fixes and didn't upgrade to a new version. Whether or
7.
by imurray 4mo ago
For those wanting alternatives to KL-divergence, the KL and Jensen–Shannon divergences are both F-divergences: https://en.wikipedia.org/wiki/F-divergence
8.
by imurray 6mo ago
Reminds me of https://cr.yp.to/djbdns/ipv6mess.html Which has been discussed previously: https://hn.algolia.com/?q=The+IPv6+mess
9.
by imurray 6mo ago
I think that's meant to be covered by the "IPv4x when we can. NAT when we must" part, in particular "ISPs used carrier‑grade NAT as a compatibility shim rather than a lifeline: if you needed to reach an IPv4‑only service
10.
by imurray 7mo ago
Ooof, I'd never seen that. Thanks! From the wikipedia link: > The May 1799 test at Oakengates carried a party of investors aboard the vessel, who nearly suffocated before they could be freed. (!) ...and eventually they built a fligh
11.
by imurray 7mo ago
The Falkirk Wheel is cool and a fun trip, along with the nearby Kelpies, which were much more striking in person than I'd anticipated. The wheel is a one-of-a-kind, but there are other ways of avoiding having a ladder of flood locks, s
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by imurray 11mo ago
The site didn't load for me in Firefox, but I found these fantastic for preventing climbing shoe stink: https://bootbananas.com/product/original-shoe-deodorisers/ They absorb sweat, not just mask the smell.
13.
by imurray 1y ago
It's clearly hard, but there are tools for doing exploratory visualization of high-dim data. GGobi http://ggobi.org/ and all the ones that arrange points but try to get local neighborhoods correct (t-sne, umap, et al.)
14.
by imurray 1y ago
Some things that may be of interest. First relevant to the posted article: A site that has used neural nets to classify go moves that good players would probably make that weaker players (of varying ranks) would probably not: https:/&
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by imurray 1y ago
This post is for those interested high-performance matrix multiplication in BQN (an APL-like array language). The main thing I got out of it was the footnotes, in particular: https://en.algorithmica.org/hpc/algorithms&#
16.
by imurray 1y ago
A PhD thesis that explores some aspects of the limitation: https://era.ed.ac.uk/handle/1842/42931 Detecting and preventing unargmaxable outputs in bottlenecked neural networks, Andreas Grivas (2024)
17.
by imurray 1y ago
> generating DTMF tones yourself and injecting them into the audio stream? When I was an undergrad I had an audio file for each digit and a winamp playlist for each of my frequently dialed numbers. I'd hold my (landline) phone again
18.
by imurray 1y ago
I wrote firefox and chrome extensions that do exactly what you want: Firefox: https://addons.mozilla.org/en-US/firefox/addon/redirectify/ Chrome: https://chrome.google.com/webstore/d
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by imurray 1y ago
Another machine learning paper ("ancient", 2015) where being able to exactly reverse a computation was useful: https://arxiv.org/abs/1502.03492
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by imurray 1y ago
I'm sceptical about the energy motivation, but there are multiple reasons why making invertible deep learning architectures can be interesting or useful. Cf, a series of workshops from 2019-2021: https://invertibleworkshop.g
21.
Correcting Myths in the Mapping of Cholera(maps.com)
1 points by imurray 1y ago|0 comments
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by imurray 1y ago
No, it will be about the same. The algorithm is wrong (calling write repeatedly) and -O3 isn't sufficient to rewrite that.
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by imurray 1y ago
I was asked why there are two tides a day in an interview for my undergraduate University place. I blundered through to the classic answer. This stackexchange discussion made me realize I was even more of an imposter than I thought :-).
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Does Earth have two high-tide bulges on opposite sides? (2014)(physics.stackexchange.com)
294 points by imurray 1y ago|90 comments
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by imurray 1y ago
Heh. I submitted it in Oct 2012. I submitted a few things back then, none got traction and I stopped bothering :-).
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by imurray 1y ago
A photo taken on my street (no exif) "only" gives the correct town in chatgpt and gemini, and then incorrectly guesses the precise neighbourhood/street when pushed. Gemini claimed to have done a reverse image search, but I&#x
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by imurray 1y ago
Looks neat. It would be useful to compare to other implementations: https://ann-benchmarks.com/ -- potentially not just speed, but implementation details that might change recall.
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by imurray 1y ago
That nice benchmark shows that multiple implementations of HNSW perform differently (my experience also). It would be helpful therefore if HANN benchmarked its implementation against the others, and tried to get the details the same as the
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by imurray 2y ago
Every product has its hate, but everyone is rarely true. Personally (no longer at Amazon) I was impressed by Chime. It was simple, but rock solid, handling large calls well. Teams is still worse for me (>9 people display is bad, even i
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by imurray 2y ago
Nelder–Mead has often not worked well for me in moderate to high dimensions. I'd recommend trying Powell's method if you want to quickly converge to a local optimum. If you're using scipy's wrappers, it's easy to sw

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