I also own https://stonks.money and am looking for good ideas for what to do with it

Comments URL: https://news.ycombinator.com/item?id=45755759

Points: 6

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Comments URL: https://news.ycombinator.com/item?id=41310226

Points: 2

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Source code is here: https://github.com/fwilliams/unwind

Paper is here: https://arxiv.org/abs/1904.04890

It got accepted to Chi 2020 which was cancelled so the paper never got presented, sadly!

Comments URL: https://news.ycombinator.com/item?id=36341638

Points: 7

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Comments URL: https://news.ycombinator.com/item?id=34276047

Points: 1

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The goal of the library to have an extremely simple API for geometry processing which uses NumPy arrays as a core data structure (so it can be dropped into whatever numerical codebase you're working with).

Most of the library is written in C++ using a custom binding framework (https://github.com/fwilliams/numpyeigen) that I wrote which avoids copies when converting NumPy arrays to Eigen Matrix types.

Happy to answer any questions you might have about it and I hope Point Cloud Utils is useful to you!

Comments URL: https://news.ycombinator.com/item?id=34261699

Points: 4

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[1] https://github.com/pybind/pybind11 [2] https://github.com/fwilliams/numpyeigen

I grew up in Stanstead. I have fond memories of story time as a child in the library, borrowing movies and comic books, and playing age of empires 2 with my best friend on the two shared computers in the front room.

There’s also a street in the town (aptly named Canusa st.) which is half in the US and half in Canada. Interestingly, the houses on one side have flags reminding you where you are, while there are no flags on the other. Figuring out which side is left as an exersize to the reader ;)

Comments URL: https://news.ycombinator.com/item?id=33206651

Points: 3

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Unfortunately it doesn't work on Firefox on Ubuntu 20.04 with an NVIDIA GTX 3090Ti. :(

For example (Emphasis mine):

> The risks of data memorization, for example, the ability to extract sensitive data such as valid phone numbers and IRC usernames, are highlighted by Carlini et al. [41]. While their paper identifies 604 samples that GPT-2 emitted from its training set, we show that over 1 of the data most models emit is memorized training data. In computer vision, memorization of training data has been studied from various angles for both discriminative and generative models Deduplicating training data does not hurt perplexity: models trained on deduplicated datasets have no worse perplexity compared to baseline models trained on the original datasets. In some cases, deduplication reduces perplexity by up to 10%. Further, because recent LMs are typically limited to training for just a few epochs

> But even putting aside the fact that claiming someone else's writing as one's own is wrong, the value in survey papers is in how they re-frame the field. A survey paper that just copies directly from the prior paper hasn't contributed anything new to the field that couldn't be obtained from a list of references.

Good survey papers can be important contributions in their own right (e.g. [1]). A good survey should contextualize works within a subject area with respect to each other and identify high level trends/ideas in that subject. These connections are not only useful for learning a topic, but also for positioning novel work or identifying under-researched areas to focus on.

If the authors felt that one of the papers they plagiarized concisely expressed what they wanted to say, they could simply quote and cite that work. Otherwise, it could be construed that the authors are claiming to be the ones drawing the conclusions they wrote. Moreover, from the article, the survey in question seems to be pretty egregiously plagiarizing, which deserves to be called out/shamed.

[1] https://arxiv.org/abs/2111.11426

My time horizon is longer than 5 years, and I buy broad market index funds split up as follows: 55% US large cap (e.g. VIIIX, VTSAX, SWTSX), 15% US mid cap (e.g. VMCPX), 10% US small cap (e.g. VSCPX), and 20% international (e.g. VTSNX, SWISX, VXUS).

I also highly recommend dollar cost averaging. i.e. buying a fixed amount of your portfolio at fixed periods. I have my bank do this automatically every 2 weeks. The benefit of dollar cost averaging is (1) it takes the emotion out of investing, and (2) over a long time window, more of your assets will be purchased at a low prices than high prices (because you're buying a fixed dollar amount of assets every N days, fewer you will buy fewer assets when prices are high and more assets when prices are low).

During my PhD, I got to spend time learning, and attending talks/seminars/conferences. Gaining deeper background knowledge in my field as well as learning how to quickly evaluate and explore new ideas gave me the tools to have the type of job I wanted. I'm a research scientist at an industrial lab now and quite enjoy it.

That being said, I agree with the grandparent post that doing a PhD can be a grueling experience. I had to carry the bulk of the work for many of the papers I submitted. If I took a day off, nobody would pick up the slack. Tight deadlines meant the only way to succeed was putting in long hours. My advisors were also spread very thin so it was difficult to get a lot of time with them. There were times when I felt very alone. This was a really stark contrast to how collaborative engineering in industry was and I don't think I ever fully adjusted to it. My current job feels like a happy middle ground. I publish papers alongside other people and we split the work.

Comments URL: https://news.ycombinator.com/item?id=22192986

Points: 2

# Comments: 0

The major contribution of the work is showing that ResNet needs a number of parameters which is polynomial in the dataset size to converge to a global optimum in contrast to traditional neural nets which require an exponential number of parameters.