Weird indeed. And I've met all of them! What are the odds? (:
3,072 karma · joined June 28, 2013
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X-Maps: Using a tiny laser projector and an event camera to estimate depth at 60 Hz on a laptop CPU. The algorithm is using only the time stamps of when the laser passes over the scene, not its color or intensity, so you can project any content you like. Fun for AR demos!
https://fraunhoferhhi.github.io/X-maps/
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Self-Organizing Gaussian Grids: 3D data is awkward to compress. But there's plenty of solutions for compressing 2D data (images!). So let's organize our 3D data into a 2D grid, where grid neighbors are also close in 3D. That's a hard problem, but we can leverage novel assignment algorithms with GPU power for parallelizing that, to get the sorting done in a few seconds.
https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/
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https://www.3dgs.zip/ resources on 3D Gaussian Splatting from our research group.
https://survey.3dgs.zip/ - a comparison of different compression methods for 3D Gaussian Splatting scenes.
I do see the tradeoff between spending more time discussing the project vs perfecting its technical aspects though. This can be tough for us techy folks who don't really like evangelism. In this respect, I believe academics and technical founders face quite similar challenges.
On another note, the feedback in the replies here has been incredibly uplifting, and I want to express my sincere gratitude for that.
But I sat down and completely rewrote all the scaffolding around the presented algorithm, to make for a smooth and fast demo. Wrote up documentation for the implementation, gave it a command line interface, set up a script to reproduce the measurements in the paper with a single execution (downloading the required data and comparing several methods). Broadcast in all channels available to me (institution press release, LinkedIn posts, GitHub awesome list linkings). And I actually brought the demo to the workshop, which wasn't required at all, I could have just presented it as a poster.
The feedback of the people seeing the polished demo was fantastic, but now I'm sitting here with my 5 GitHub stars on the repo, after I put like 2.5 extra months of work into it. Maybe it led to more people noticing it, and finding it useful. Maybe not.
Getting stuff out there to be noticed is difficult, a lot of work, and going the the last 10% does not guarantee any more success.
In a world where potentially world-changing experiments are performed by a person who interjects their material science comments with gems such as "USSR is still the primary progressive power worldwide" and "My ass is on fire and it lights the way for people. To infinity!", satire as subtle as an Elon Musk name change just doesn't seem to cut it anymore.
Silicon Valley Disruption Delusion - This peculiar state of mind is characterized by an individual's inclination to perceive every life aspect as a sector ripe for modernization. Symptoms include an increased use of entrepreneurial vernacular, spontaneous pitching to unsuspecting venture capitalists, and a tendency to self-identify as a "founder." In extreme cases, one might even start praising the virtues of blockchain for everyday activities.
Amsterdam Cycle Confusion - In this unusual psychological state, the individual develops a belief that they should travel exclusively by bicycle. This can lead to fervent cycling even in non-bike-friendly areas and a distinct reluctance to use pedestrian pathways or motorized transport.
Munich Brewmaster Belief - Individuals affected by this syndrome are consumed by the idea that they are master brewers. The condition manifests in an incessant discussion about hops and yeast, an urge to experiment with brewing in unconventional locations, and the staging of impromptu beer tasting competitions.
Palo Alto Unicorn Unreality - Those affected by this syndrome exhibit an uncanny tendency to transform every idea into a potential billion-dollar startup or 'unicorn.' They might display irregular sleep patterns, subsist mainly on energy drinks and quick meals, and their conversations are often peppered with phrases like "the next big thing," and "exit strategy."
Seattle Server Overload Syndrome - This cognitive anomaly leads a person to believe they're akin to a server, required to handle multiple requests concurrently. They may develop an unhealthy penchant for multitasking and often describe their mental state using terms such as "processing," "bandwidth," and "buffering."
The water was freezing, she spent a month sneezing, but said she would do it again. Here's to the ones who dream, foolish as they may seem. Here's to the hearts that ache, here's to the mess we make.
Unfortunately I'm not deep enough into the topic to understand what their contribution to the theory part of it is. (they have some Supplementary Material in [INN Supp]). In the discussion of the Integral Neural Networks (INN) paper, there's this paragraph about an operator learning publication:
"In [24] the authors proposed deep neural networks with layers defined as functional operators. Such networks are designed for learning PDE solution operators, and its layers are continuously parameterized by MLPs only along the kernel dimensions. A re-discretization was investigated in terms of training on smaller data resolution and testing on higher input resolution. However, the proposed framework in [24] does not include continuous connections between filters and channels dimensions."
Also the weight permutation to perform the resampling on pretrained networks in INNs seems to be novel? And I guess it doesn't hurt that they're bringing new eyeballs to the topic, by providing examples of common networks and a PyTorch implementation.
[INN Supp]: https://openaccess.thecvf.com/content/CVPR2023/supplemental/...
[24]: Zongyi Li Nikola Kovachki. Neural operator: Graph kernel network for partial differential equations. arXiv preprint arXiv:2003.03485, 2020, https://arxiv.org/abs/2003.03485
My understanding of this work: A forward pass for a (fully-connected) layer of a neural network is just a dot product of the layer input with the layer weights, followed by some activation function. Both the input and the weights are vectors of the same, fixed size.
Let's imagine that the discrete values that form these vectors happen to be samples of two different continuous univariate functions. Then we can view the dot product as an approximation to the value of integrating the multiplication of the two continuous functions.
Now instead of storing the weights of our network, we store some values from which we can reconstruct a continuous function, and then sample it where we want (in this case some trainable interpolation nodes, which are convoluted with a cubic kernel). This gives us the option to sample different-sized networks, but they are all performing (an approximation to) the same operation. After training with samples at different resolutions, you can freely pick your network size at inference time.
You can also take pretrained networks, reorder the weights to make the functions as smooth as possible, and then compress the network, by downsampling. In their experiments, the networks lose much less accuracy when being downsampled, compared to common pruning approaches.
Paper: https://openaccess.thecvf.com/content/CVPR2023/papers/Solods...
I payed 5€ for Machato, and would probably pay up to $10 to try out other apps. For me, $30 would already be in a territory that I would not pay upfront, but only after regularly using the app for at least a few weeks. That's pretty much what I paid for Apollo Ultra Lifetime, after many months of regular use. Oh dear.
And as far as features go, I don't see a way to set the temperature.
Edit: had a closer look, it's a multi-app for different AI chatbots. Fun, but not at all what I'm looking for (responsive Mac interface to OpenAI API).
So far I've been using Machato (https://untimelyunicorn.gumroad.com/l/machato), which has the perfect feature set for me, but despite its claims of being a native app, is just infuriatingly slow. You can't at all scroll in the app while it's producing text, and switching between conversations feels worse than using Slack.
So the search continues. What makes it so hard to produce a responsive native app, that there are so few of them out there?
3 * 2 ^ {0, 0, 6, 7} - 1
And all of them are palindromes.
On a more serious note, I'd be super interested to know how high the percentage reddit users who don't speak any English is. And how high it could grow in the future. I'd wager that most of the people that hang around r/de and r/france are also members of English-speaking subs (even if passive). That's because the interesting thing about reddit is that you have all these niche communities of your interests that you could join. But if you fragment these niches further down by language, you end up with communities that are too small to be interesting. E.g. r/Elektroautos/ just does not have enough content, compared to r/electricvehicles/
> Has driven changes in upstream Rust: more try_ methods for Vec that don't panic in OOM: https://github.com/rust-lang/rust/pull/95051
I was curious to have a look at that PR, but it seems it was closed after a long discussion (mainly because it would add ~30% more methods to Vec?). So which changes landing in upstream Rust is the bullet point referring to? Was the Keyword Generics Initiative born out of this?