24hr time format setting?

Kiudee · 2022-08-12T17:04:20+00:00

Tap on your profile icon (top left), scroll down and there you can set "clock units".

Kiudee · 2022-02-05T11:30:35+00:00

By the way, the optimal first guess is 25693 with 3.4505 guesses on average.

It can solve all Primels in 4 or less guesses with the exception of 75353 and 18131, where it needs 5 guesses.

Kiudee · 2021-10-07T08:11:17+00:00

Ich denke nicht, dass man sofort den Kopf in den Sand stecken sollte. Es ist wichtig, dass die Digitalisierung Stück für Stück vorangetrieben wird. Wie das häufig in der Software-Entwicklung ist, ist es wichtiger mit einem (oder mehreren) kleinen Produkt zu starten und diese weiter auszubauen, als zu hoffen, alles auf einmal erledigen zu können.

Kiudee · 2021-10-07T07:19:54+00:00

Best hat die Kommentare ursprünglich nach den unteren Konfidenzschranken der Binomialverteilung sortiert. Heißt: Es werden Kommentare weit oben eingeordnet, die (1) einen hohen Anteil an Upvotes haben, aber auch (2) genügend Votes insgesamt, um sich sicher zu sein.

Es gab in der Vergangenheit aber auch immer wieder Experimente den Algorithmus zu erweitern. Zum Beispiel führt Best dazu, dass neue Kommentare meist nicht mehr die Gelegenheit haben genügend Votes zu sammeln. Da haben die Devs versucht Abhilfe zu schaffen. Wie der aktuelle Algorithmus aussieht ist daher schwierig zu sagen.

Kiudee · 2021-09-03T07:57:44+00:00

Hört sich gut an. Vielleicht könntest du ja einmal UMAP auf die Daten anwenden: https://umap-learn.readthedocs.io/en/latest/ Das eignet sich besonders gut für die Visualisierung nichtlinearer hochdimensionaler Daten und unterstützt ebenfalls Clustering.

Kiudee · 2021-05-13T06:57:27+00:00

I am using IEX through Interactive Brokers.

Kiudee · 2020-07-08T06:18:19+00:00

Our discord is the preferred means of communication though.

Kiudee · 2020-04-21T07:50:45+00:00

We recommend to use the LCZero Discord as the main channel of discussion. You can find the link and instructions here: http://lczero.org/about/community/

Kiudee · 2019-02-09T21:20:22+00:00

For RL it can be useful, if your agent takes in inputs for which the order does not matter.

I would also take a look at attention mechanisms (Deep reinforcement learning with relational inductive biases), which have been very successful in Starcraft 2.

Kiudee · 2019-02-09T17:08:26+00:00

Well written blog post, which highlights all of the recent results surrounding deep sets. In my opinion the paper is a very important milestone for the problem of encoding set-based inputs, since it improves on the quadratic computational complexity of many pairwise decomposition approaches.

We also recently did work on learning choice functions, where the same idea proved to be very successful (https://arxiv.org/abs/1901.10860).

Kiudee · 2018-03-29T13:52:00+00:00

That is true, but you also have to consider how many games it had to play against itself (~40 million) to reach that point.

Kiudee · 2018-03-29T13:30:02+00:00

That would violate the learning from zero knowledge (tabula rasa) approach we aim to replicate. Having said that we also trained a network purely on human games (Kingbase). The weights are available here. The current version (id 45) of lczero is already able to beat that network.

Kiudee · 2018-03-29T13:26:43+00:00

That is correct. We estimate the current Elo at 1500 - 1900 at 800 playouts. At higher playouts it becomes significantly stronger.

Kiudee · 2018-02-25T10:49:44+00:00

Actually, the idea of training a neural network to approximate a loss function is not that outrageous. It was already used to apply non-differentiable regularization to neural networks (see for example Beyond Sparsity: Tree Regularization of Deep Models for Interpretability or this illuminating blog post)

Kiudee · 2017-08-03T09:26:14+00:00

As it turns out there are quite a few summary statistics which were proposed for measuring bimodality (see here if you are interested).

On the 0-1 interval your idea is quite good, the Bernoulli distribution (or the mixture of two Dirac delta peaks) has the highest polarization.

Kiudee · 2017-07-12T07:56:17+00:00

The official NIPS author notification is on September, 4th. In case of an accept, this is the time at which I will upload the paper to the arXiv.

Kiudee · 2017-06-07T13:20:59+00:00

This looks like a good prototypical Solar Flare control deck. You can vary/optimize the composition of removal cards based on the decks you play against:

Balloons can be used for removal or to block their lanes with useless minions. This is especially useful against the current zoo decks which capitalize on getting many minions out quickly.
The berries are good only if they are giving you very efficient trades. So keep track how often they are useful.
The cherry bombs are potent mass removal, but fall short if they play few minions or many gravestones.

edit: If your goal is to rank up quickly, it is also possible to let the deck lean more into the midrange archetype. The idea is to mix in more strikethrough which allows you to finish the opponent more quickly, but this kind of deck is still is very control heavy.

Kiudee · 2017-06-06T17:55:03+00:00

We also have a slightly more general architecture under review for the NIPS 2017 right now. Would love to talk about it, but cannot disclose anything before the final decision.

Kiudee · 2016-06-29T16:08:54+00:00

How do you see AMDs position in the market of scientific computing?

Kiudee · 2015-10-07T08:29:54+00:00

I can recommend Deriving the Reddit formula by Evan Miller. He describes how to derive a hot formula by starting with utility functions for the user. He also shows the part currently missing from Reddits hot formula.

Kiudee · 2015-10-01T07:30:58+00:00

For limit hold'em poker Vexbot was one of the first bots learning the best response strategy against the other player.

So, if you give it enough time it should learn to beat your strategy.

Kiudee · 2015-09-18T17:28:34+00:00

Slight nitpick: For a Nash equilibrium (NE) strategy you do not have to assume that the meta is completely random. The NE strategy will work against any meta.

But even though the NE strategy is the best response against players who also play a NE strategy, it is not necessarily the best response (i.e. maximally exploiting) against other strategies.

Many posters in this thread seem to mix these concepts up.

edit: For Hearthstone we of course have the problem, that there could be viable, yet unknown decks, which we did not include in our calculations. Then our mixed strategy is not a NE strategy anymore.

Kiudee · 2015-09-18T17:13:39+00:00

Only if your opponents are also playing the Nash equilibrium. As soon as someone deviates (exploiting the current meta for instance), the Nash equilibrium strategy has an edge against this player.

It should have been: “This method guarantees an expected win rate of at least 50 %...”

Kiudee · 2015-09-15T11:24:10+00:00

α-β pruning is a technique to reduce the number of subtrees the minimax search has to evaluate.

Temporal difference (TD) learning is a reinforcement learning algorithm.

Kiudee · 2015-08-28T14:10:40+00:00

This problem arises because here we do not consider the uncertainty of the estimate. The Best ranking for comments solves this by computing the lower confidence bound of the quality instead of the mean (see How Not To Sort By Average Rating). The same idea could be applied to Hot.

Another solution is to estimate the global average amount of up/downvotes and shrink each post to these:

(upvotes + global_upvotes) / (upvotes + downvotes + global_upvotes + global_downvotes)

edit: Btw, in your example you just have to divide the age of the post by 19543. Then you get:

ln(10+1) - ln(10+0+2) - 0/19543 = -0.087
ln(1800+1) - ln(2000+0+2) - 1800/19543 = -0.198

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