orange3xchicken

10 ms·

1.
by orange3xchicken 4y ago
You probably couldn't find much evidence re Krugman + Charlatan, because he's not. He's an accomplished economist who's influential and respected among the academic community. He's won two of the most prestigious aw
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by orange3xchicken 4y ago
At least on the quant side, I think the typical sentiment is that most researchers aren't interested in ops / developing infrastructure / curating datasets.
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by orange3xchicken 4y ago
It sounds like you aren't really interested in a rational discussion by the second half of your post, but the typical arguments (incl in the post) for are that market makers reduce inefficiencies in the market & provide liquidity t
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by orange3xchicken 4y ago
I know, I've been waiting so long for this! I will not miss having to render and reference pngs of equations / api requests.
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by orange3xchicken 5y ago
I think K is usually interpreted as the # of gaussians from which the data has been assumed to be sampled. Not immediately related to # of latent variables unless you invoke kernel kmeans or something like laplacian eigenmaps/diffusion
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by orange3xchicken 5y ago
The term "labeled graph" just means a graph with each node labeled differently (but arbitrarily). It just allows for reasoning & enumerating the vertex set. It's a typical assumption to make in the context of graph isomor
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by orange3xchicken 5y ago
That's really cool. These days similar strategies based on graph coarsening and vertex ordering are really popular for improving the sparsity pattern of preconditioners- e.g. incomplete lu decompositions for iterative linear solvers.
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by orange3xchicken 5y ago
Just last night I noticed that the zoom program on my mac has been suffering from this bug, which is ~4 months old. Both are up to date. https://community.zoom.com/t5/Meetings/Why-is-the-Zoom-app-l...
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by orange3xchicken 5y ago
I'm less familiar with poisoning, but at least for test-time robustness, the current benchmark for image classifiers is AutoAttack [0,1]. It's an ensemble of adaptive & parameter-free gradient-based white-box and gradient-free
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by orange3xchicken 5y ago
A new subfield of adversarial ML that considers similar challenges to adversarial NLP: topological attacks on graphs for attacking graph/node classifiers. Both problems (NLP & graph robustness) are made much more challenging compar
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by orange3xchicken 5y ago
Recommend Russel's Human Compatible. Three principles to guide AI development: 1. The machine's only objective is to maximize the realization of human preferences. 2. The machine is initially uncertain about what those preferences
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New Kavli Center at UC Berkeley to foster ethics, engagement in science(news.berkeley.edu)
20 points by orange3xchicken 5y ago|8 comments
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by orange3xchicken 5y ago
Maybe Bosch? Prof. Zico Kolter from CMU is a chief scientist associated with them, and his group does a lot of really good work in the ml verification space (e.g. the first randomized smoothing and the Wong & Kolter certificates).
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by orange3xchicken 5y ago
For anyone interested in elegant implementations of state of the art algorithms for verification, there is a nice library in Jax: https://deepmind.com/research/open-source/efficient-and-tigh...
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by orange3xchicken 5y ago
Not sure if this is what you mean, but I found an ongoing Wikimedia research project & preprint: https://meta.wikimedia.org/wiki/Research:Link_recommendation... https://arxiv.org/abs/2105.15110
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by orange3xchicken 5y ago
Not arguing with your point, but the blame shouldn't entirely be attributed to the research community. It's easy to read about the various parasitic practices conducted by academic & government administrations. People who do s
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by orange3xchicken 5y ago
I think emulating existing darknet market infrastructure makes sense to address a couple of the issues you mentioned- like public reviews & messaging for employment + moderation & escrow/multisig for transactions. + your third
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by orange3xchicken 5y ago
There is a lot of fundamental work on random projections [0] from Dasgupta et al., Achlioptas et al., etc. For example, while random projections produced by sampling normal random variables like in the article are sufficient, there alternat
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by orange3xchicken 5y ago
This is basically adversarial training, which is a typical (& very practical) benchmark heuristic defense for this problem. An ongoing question is to precisely characterize when and how AT works. The line of work has also proved to be v
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by orange3xchicken 5y ago
It turns out that a similar technique, where you basically apply noise multiple times to a single image, and average predictions over all noisy images- equivalent to convolving your nn with Gaussian noise yields near state of the art bounds
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by orange3xchicken 5y ago
fyi the founder Igor Carron runs a pretty nice academic blog on compressive sensing (a bit less academic in recent months) https://nuit-blanche.blogspot.com/ Also hosts the advanced matrix factorization jungle website whic
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by orange3xchicken 5y ago
Someone correct me, but I think hMetis is one popular software/algorithm for multi-level graph/hypergraph partitioning. There is also KaHyPar which is a bit more academic.
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by orange3xchicken 5y ago
Practical edge crossing minimization for large graphs (e.g. a billion nodes) is a really hard problem. An alternative formulations that's seen some use in graph drawing & circuit design software is to describe the embedding problem
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by orange3xchicken 5y ago
Relevant paper from Misha Belkin's group https://arxiv.org/abs/1909.12362
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by orange3xchicken 5y ago
I wanna agree with this. I've seen a bunch of those animations before..e.g. https://www.deviantart.com/kirokaze and it would probably be great to give the original artists a shout out.
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by orange3xchicken 5y ago
Not necessarily for or against the work, but it's not just him working on this project solo like some mad scientist. There are a couple of other members on his team with academic publication histories as well.
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by orange3xchicken 6y ago
I wasn't talking about methods for matrix multiplication. The algorithms that are used by most ad recommendation services are deep enough themselves. The ICML test of time award this year was for an adaptation of a technique used to st
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by orange3xchicken 6y ago
Some tier-A mathematicians working on some pretty fucking cool problems in online decision making and machine learning, or someone who writes shit like: "Young people these days, I am told, are illiterate and cannot understand the writ
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by orange3xchicken 6y ago
To add on: Zeyuan Allen-Zhu and Yuanzhi Li had a great theoretical paper earlier last year proving a fundamental discrepancy between the concept classes learnable by kernel methods and deep learning models. https://arxiv.org&#x2F
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by orange3xchicken 6y ago
One cool solution the wikimedia research team has been working on is to apply multilingual nlp. Like, how can someone detect a discrepancy in information presented in an article written in english & the same article written in russian?

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