aglionby
Joined 9 years ago
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244
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98
Member since
Oct 30
Recent submissions
Comment · 4 years ago
I don't know - with some edge detection I think you could distinguish between the faces of each die, and some heuristics would probably get you the upwards-facing one. Another thing that may be useful is that in each image the only face that is fully visible for all dice is the upward one (who knows if that is just for this example though).
Comment · 5 years ago
This is great! Especially nice to be able to remove entire fields.
Relatedly, here are a couple of tools to ensure that references are complete (e.g. updating arXiv papers to their published versions, mostly for computer science papers):
- https://github.com/yuchenlin/rebiber (CLI, web interface)
- https://www.cl.cam.ac.uk/~ga384/bibfix.html (only *ACL papers, web interface with diff, disclaimer: mine)
Comment · 5 years ago
Great post, entertainingly written.
Back in 2015, Instagram did a blog post on similar challenges they came across implementing emoji hashtags [1]. Spoiler alert: they programmatically constructed a huge regex to detect them.
[1] https://instagram-engineering.com/emojineering-part-ii-imple...
Comment · 5 years ago
I'm guessing they won't come unless you chase it. Nothing happened to my refund for May flights until I called, after which the money came within a week.
Comment · 6 years ago
It isn't fair (in the statistical sense) to make a comparison on the basis of rank between an over- and under-represented group. Interesting read here https://en.chessbase.com/post/what-gender-gap-in-chess
Comment · 6 years ago
2002 was the year of the Nokia 7650, their first phone with a camera - 0.3MP which it displayed on its 176x208 screen. It cost €740 in 2020 money (€550 back then).
In 2020 you can buy a second generation iPhone SE for €489, with a 1334x750 screen (at >6x pixel density) and 12MP camera.
These are not roughly equivalent pieces of hardware, and the software they run differs immensely. MMS is not Instagram.
Comment · 6 years ago
> system where I actually refer back to things because I seem to google for the same things over and over again
Yep, this often frustratingly turns nothing up for me. Hence, Pinboard :)
Comment · 6 years ago
I have two use cases. The first is to keep track of interesting articles I find and plausibly want to refer back to in the future. A 3rd party browser extension and mobile app make saving very easy, and then I tag each item with a high-level category. This is also pretty painless, and brings a lot of value (otherwise you just have an unsorted collection of links - not helpful). An example is my 'long reads' tag https://pinboard.in/u:guyaglionby/t:long-read/. The 'unread' feature is also useful here - I've got >10 long reads banked for when I'm looking for things to do.
The second is as a kind of mechanism to give myself permission to close a bunch of tabs every time they accumulate. Each is _obviously_ open for a good reason and I may want to read it at some point, so sticking it on pinboard is a nice way of shoving them elsewhere. I don't save everything - curation is important (in the same way as with tagging). Lots of what remains are things that may be useful for me in the future but are not immediately, like design guides https://pinboard.in/u:guyaglionby/t:design/. Some of these things I leave as 'unread'; others that feel more like reference material I mark as 'read' immediately so as not to have them in my to-read queue.
Comment · 6 years ago
My background is in NLP - I suspect we'll see similar in language processing models as we've seen in vision models. Consider this[1] article ("NLP's ImageNet moment has arrived"), comparing AlexNet in 2012 to the first GPT model 6 years later: we're just a few years behind.
True, GPT-2 and -3, RoBERTa, T5 etc. are all increasingly data- and compute-hungry. That's the 'tick' your second article mentions.
We simultaneously have people doing research in the 'tock' - reducing the compute needed. ICLR 2020 was full of alternative training schema that required less compute for similar performance (e.g. ELECTRA[2]). Model distillation is another interesting idea that reduces the amount of inference-time compute needed.
Comment · 6 years ago
There's some similar work out that analyses the impact on conference paper acceptance of having deanonymised arXiv versions of papers available before review. They look at ICLR papers for the last 2 years.
I've not read it in a lot of detail but it looks like there's a positive correlation between releasing papers and having them accepted. Not sure how they've controlled for confounders (you only release papers you're confident in the quality of on arXiv?) https://arxiv.org/pdf/2007.00177.pdf
Comment · 6 years ago
Home and end are most useful for me when I'm writing or programming, so both of my hands are on the keyboard anyway. I think I actually find these key combos more useful than a dedicated home or end button, as I'd have to move my hands a lot further for those.
Comment · 6 years ago
Dominos in the UK has similar incentives but put in place less rigidly - any medium/large pizza will run you £18-£20, but there are loads of 40%-50% discount above £x (x >= 35) or 2-4-1 deals. This makes it more difficult to justify a solo order (unless you get 2 pizzas, which, fair enough) and incentivises ordering with friends or not at all.
Comment · 6 years ago
This problem of inferring something's truthfulness based on the number of times it's written down is nicely explored in the paper "Reporting Bias and Knowledge Acquisition".
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