bearzoo

12 ms·

1.
by bearzoo 3y ago
somewhat related is the number theoretic transform https://ieeexplore.ieee.org/abstract/document/1451721
2.
by bearzoo 3y ago
think the author is talking about having to fix the extra vector in V to be zeros and making sure to not compute/apply gradients to it
3.
by bearzoo 5y ago
I think you are going to confuse people by framing this as "time series" and then focussing on reinforcement learning. A lot/most of your framing is around RL. from a first glance and a read through your roadmap, this does no
4.
by bearzoo 5y ago
Eh I feel like may be worth cutting some slack here.. It sounds like you’d like the title to be prefaced with “one time that”. I don’t think anyone reading this says, hey gans are done for. I doubt the authors think that and I (clearly) don
5.
by bearzoo 6y ago
I don't think people have given up on model based RL, it is just that describing a proper model is (like you are saying) very difficult. in the case you haven't seen or read the following: https://bair.berkeley.edu&#x2F
6.
by bearzoo 6y ago
many scientific computing applications are considered to be bounded by io
7.
by bearzoo 6y ago
There are a lot of different definitions of intrinsic value. But it often has nothing to do with how much someone will give you. There is the asset, an estimation of what the asset is truly valued at (intrinsic), and then finally the market
8.
Functional Programming in Python(github.com)
145 points by bearzoo 6y ago|62 comments
9.
by bearzoo 6y ago
Do you have any evidence that they are lying about all of the use cases they list on that web site?
10.
by bearzoo 6y ago
https://eng.uber.com/machine-learning/
11.
by bearzoo 6y ago
actually, I think this is exactly what misappropriation theory is supposed to account for. can you explain more around why it is well within their rights? I don't think it is.
12.
by bearzoo 6y ago
Svms are, by default, linear models. The decision boundary in the Svm problem is linear and since it’s the max margin we may enjoy nice generalization properties (as you probably know). You probably also know that decision tree boundaries a
13.
by bearzoo 6y ago
https://distributed.dask.org/en/latest/efficiency.html#don-t...
14.
by bearzoo 6y ago
in NYC una is the best i've found. it easily competes with the best in naples (in my opinion)
15.
by bearzoo 6y ago
Thanks, that is pretty impressive
16.
by bearzoo 6y ago
Oh? I don't suppose that julia wraps lapack functions for no good reason. What makes you say this
17.
by bearzoo 6y ago
The flaw, in my opinion, is not necessarily in the benchmark but in the claims and observations around the benchmark. For instance the blog flat out says: > Nope, we work with float32. It seems that is not true. The blog should make clea
18.
by bearzoo 7y ago
This is a bizarre analysis in my opinion. It’s not all about the mortality rate, it’s also about spending in general.
19.
by bearzoo 10y ago
Have you experimented with replacing the averaging operation on the vectors with a recurrent network such as an LSTM. This way you can not ignore the temporal nature of the feedback (I have had success improving metrics doing this on implic
20.
by bearzoo 10y ago
I would email greg brockman. I pestered him for advice one time and the amount of time he took to respond (and the amount of times he responded) was well beyond my expectation.
21.
by bearzoo 10y ago
A "solved" problem? I think that is a ridiculous thing to say. Just because people know how to parse json does not mean there aren't solutions that are more performant or useable. Shit maybe they just wanted to parse and coll
22.
by bearzoo 11y ago
thanks for all the great responses!
23.
by bearzoo 11y ago
With spearmint I had the ability to modify the parameters of the mcmc sampling (e.g. burn in iterations). Will sigopt expose parameters for those of us who want to manipulate them? Will there be options to use different types of function e
24.
by bearzoo 11y ago
https://github.com/JasperSnoek/spearmint
25.
by bearzoo 11y ago
Well - just my two cents. The title feels inaccurate. You all are tuning hyper parameters with respect to the performance of the classification task. The bayesian optimization is really to optimize the unsupervised -> supervised pipeline
26.
by bearzoo 11y ago
For work a month ago I implemented stochastic gradient descent in some arbitrary directed graph of operations (I applied a skill I learned from neural networks in a system that had nothing to do with neural networks) which required me to kn
27.
by bearzoo 11y ago
I did not claim anything about the underlying data.. The 2 dimensional embeddings were forced into the unit circle because the 'perplexity' hyper parameter for the t-sne was set too high. From the guy who helped make t-sne: When I
28.
by bearzoo 11y ago
i once read that high perplexity can generate embeddings that are very tightly bound on a unit circle...not sure if this is what is going on
29.
by bearzoo 11y ago
I REALLY REALLY want someone to do this with a huge amount of google image thumbnails so that the google crawlers just start hitting google servers. Would it be considered malicious to do such a thing?
30.
by bearzoo 11y ago
Contrary to what many people will tell you - a degree is just ONE of the things you go to college for. Go to college. Your at a great age, and will have experiences you will not be able to have at any other time in your life. It is worth it

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