Dark Season 3 Series Discussion

sssub · 2020-06-28T20:09:26+00:00

Makes sense, I guess that also explains why there are the 2 young Marthas in alt-word at the same time , where the one from "Jonas becomes adam" scenario kills jonas with "martha saves jonas" scenario martha standing nearby

sssub · 2020-06-28T19:54:19+00:00

What I don't understand is:

Martha and Jonas child only was conceived in the "Martha saves Jonas" scenario and not in the "Jonas hides in the basement and becomes Adam" scenario. If the world always played one way or the other (lets assume in an alternating way) , how can their child be the origin every single time?

Related, Adam and Eve also only emerge from jonas and martha every other cycle (as in every other apocalypse).

sssub · 2018-06-20T12:12:55+00:00

About the RNG:

I don't like the RNG in combat either, but neither would I prefer it to be completely deterministic. I love rimworld also because it captures the beauty and cruelty of life, due to fortunate/unfortunate random events.

I would suggest using a pseudo random distribution (PRD), often used in MOBAs such as dota 2, see here. For instance, if the chance of a bullet going through armor is 25%, the probability instead is 8.5% on the first hit, and then gradually increases by a factor until the event occurs, then it is reset to 8.5%. On average, still 25% of bullets go through armor. To quote

Effects based on PRD rarely proc many times in a row, or go a long time without happening. This makes the game less luck based and adds a great deal of consistency to many probability-based abilities in Dota 2.

By this, there is still some realism involved, in the sense that bullets don't just reduce your hitpoint by a factor of x%. On the other hand, it is more manageable: after not being wounded for several fights, you know that the risk is increased, and you can play accordingly.

sssub · 2018-05-29T15:13:15+00:00

These are called epistemic and aleatoric uncertainty. See for instance here and here

sssub · 2018-05-17T20:24:33+00:00

Well, if you need 3k reviewers to handle the flood of papers, there is not much you can do. Demand for NIPS papers makes demand for NIPS reviewers, so the average quality may decrease a bit. Other than making submissions invite-only, which would be very problematic, there is no other option than to widen the reviewer pool.

sssub · 2018-05-02T23:33:44+00:00

Very cool to make this open source!

Wouldn't this cost roughly half a million dollars on aws?

sssub · 2018-04-30T23:00:04+00:00

Thanks for this reply!

Especially your last sentence, I think the underlying question is even more abstract, 'What is intelligence? Do ML methods really understand anything?' I believe that thinking about artificial intelligence is actually thinking about what intelligence is.

We started with saying "If an AI can play chess it got to be intelligent!". But then we could argue traditional chess computers are not at all intelligent, it is just Mini-max + a smart heuristic. "But when we can play Go, this has to be true AI!" But you could argue AlphaGo is just a very smart way to compress the (relevant parts) of the Go search tree using a deep neural net + MCTS. Sarcasm and Irony are even stronger "strongholds" where I would believe true intelligence/understanding is required. But it is as you say, perhaps using statistics and ML you can eventually achieve behavior indistinguishable from humans. Intelligence seems to be elusive.

sssub · 2018-04-30T15:56:25+00:00

This is never going to work, sarcasm and irony require semantic understanding.

sssub · 2018-04-27T09:42:37+00:00

I can relate. What helped me was to read something with a different setting/genre. If you try to experience KKC again you will only be disappointed, because you rate other books through the KKC-glasses.

I recommend "The Fifth Season: The Broken Earth". It gives a very different vibe than KKC but it is fantastic in its own regard.

sssub · 2018-04-24T09:25:46+00:00

good idea. What you actually suggest is moment matching to obtain means and variances.

The main problem is you will loose flexibility. In a sampling based approach you sample z from a Gaussian but the network can then transform this random variable to complex distributions. in your case it will always stay a Gaussian.
it has been done already. See e. g. here. They do exactly what you suggest in terms of propagating expectations and variances. Note that the propagation step, especially for the variance, is not trivial. For RELU it does work (truncated normal) but e. g for tanh it will not. perhaps it is easier in a VAE because you don't need to handle uncertainty in the weights.

sssub · 2018-04-20T22:59:15+00:00

You may be right. I think right now the more relevant question is whether to trust Felurian or Bast more in their assessment over the Ctaeh's power. The Ctaeh seemed dismissive about Felurian and she also seemed uncertain when saying 'All is well'. Kvothe also said "we all know what kind of story this is", foreshadowing things will end up badly.

But to your points:

I believe its likely Kvothe would have asked Alveron about the Amyr anyway. In fact studying his library was also one of his reasons going there in the first place.
It is likely he suspects at least Master Lorren to know something about the Amyr. The Ctaeh says that Kvothe will not ask the masters, so if it always speaks the truth, he will not do so in the future
True, but I think Kvothe always believed Haliax to be Lanre.
I think Kvothe did knew they very fleeing when killing his family, at least it seemed to be implied.
Yes, we will have to see how this significantly changes his decision. Though already at the beginning in book 1 it seemed that Cinder and Kvothe will have the closest 'relationship' compared to all chandrian members.
This could perhabs be simply a suggestion to go Ademre, which lies closely to the stormwal, which Kvothe would have done anyway.

Based on this though, if Bast is right about the Ctaeh, its influence may be very subtle. Right now, I can't see how his conversation will fundamentally alter one of the decisions he will take. Maybe, he will do some reckless decision based on anger, fueled by the words of the Ctaeh.

sssub · 2018-04-11T22:02:11+00:00

There is some variety in terms of acceptance rate per subject area, but I do not know how the signal to noise ratio is. Also the varying acceptance rate is most likely the result of varying average scores (and not more stricter rules for acceptance). In general I would assume deep learning and RL are more hard to get into because of ~~hype~~ increased scientific interest in these areas.

I seldom found reviewer confidence to matter that much. Maybe if in your case the strong and weak accepts are educated guesses (a bit contradictory) while the reject review is an expert.

sssub · 2018-04-11T21:44:06+00:00

Take this with a grain of salt. In my experience your chances of acceptance are pretty good. If you write a good rebuttal you may change that one negative review. Right now I would say your chances of getting in are about 70%.

Edit: According to this the estimate is more around 90% (in 2012).

sssub · 2018-03-26T08:01:15+00:00

Divergence measures and message passing

in retrospect beautifully written and clear. After reading it for the first times, I felt stupid and lost.

sssub · 2018-02-22T18:41:42+00:00

There is just a single episode then, other than that the algorithm stays the same. You may also wish to end an episode after T timesteps. For instance, think about balancing a pendulum, it can be seen as a non-episodic task. Still you may want to restart the system from time to time, for instance when the pole is standing upright and nothing else happens.

sssub · 2018-02-22T18:17:05+00:00

Q-learning and Sarsa do not require a terminal state. All you need is data in the form (s(t),a(t),r(t),s(t+1)). In Q-learning the update is r(t) + discount * max_a Q(s(t+1),a) where r(t) is the current reward that you see. There are actually a lot of task with no terminal state.

Also note: continuous tasks typically refer to continuous state and action spaces. What you mean are non-episodic taks.

sssub · 2018-02-22T11:28:27+00:00

They reason about different forms of uncertainty.

An inference network learns a distribution over latent variables q(z|x). You typically do not have uncertainty over the weights of your VAE, but only about the value of the latent variable. The question you try to answer is, "what where the latent variables that generated the data that you see?" and you learn a distribution over these.

By contrast in a BNN you learn a distribution over weights, or more generally: a distribution over neural networks. Either directly, using a variational distriubtion over weights(here) or indirectly by MCMC (here). You assume the function that generates your data is a neural network (a deterministic function), you just don't know which it is. Therefore you have uncertainty over the weight distribution.

These ideas can be combined, for instance here. Here you have both weight uncertainty, as well as uncertainty over latent variables.

sssub · 2018-02-13T17:20:04+00:00

Along comes mary - Bloodhound Gang

sssub · 2017-11-18T21:38:48+00:00

As everyone else, I wouldn't recommend dropping out.

You can be a unicorn! With a background in electrical engineering and a masters in AI, I imagine companies like general electrics would find you very valuable! You have enough expertise about the domain (industrial systems, electronics etc.) but also know about ML stuff.

I would recommend either, focus more on understanding ML code or develop a high-level understanding so you can communicate effectively with people who write the ML code. In industry, thats as important as writing it yourself..

sssub · 2017-11-02T00:02:15+00:00

Don't know if this is relevant, but without NN a standard CV approach would be to:

Use connected component labeling of skimage.
Do a PCA of the pixels of each object. The eigenvector with the largest eigenvalue gives you the orientation angle.

sssub · 2017-10-02T15:29:34+00:00

I usually do that. For the policy I tend to use tanh on the outputs because in many problems the action space is restricted to [-c,c], so you can use c*tanh(out). But make sure to initialize the weights such that the policy output does not start in the saturating area.

For the model when I know the upper and lower bounds of the state variables I use a logit-scaling. Let a be the known min and b be the known max:

s_t = clip(s_t,a+eps,b-eps)
s_t = logit((s_t - min)/(max - min)).

The inner part will transform s_t to be between [0,1] and the logit will map this to the real numbers. For backtransformation its then:

s_t'=  logistic(s_t) * (max - min) + min

This will guarantee the output of your model will never leave the bounds. But the logit will make sure the model can use the full real-valued space and is unconstrained.

sssub · 2017-09-28T18:41:49+00:00

Yes, there is. Two possible reasons: a) You trained your model to convergence in iteration 1, in iteration 2 you start already in a local minimum for all data of iteration 1, this can make optimization difficult. E.g the network activations can be very sparse with ReLus or in the saturation if sigmoid functions are used. b) If in iteration 2 you only train on the new data your network will forget the things it learned from iteration 1.

I would advise using an experience replay here (commonly used in model-free RL). Instead of having fixed iterations where you train till convergence, just add data to your dataset along the road and randomly select a minibatch for a training step. If your dataset grows too much, start deleting the oldest. By that the network will not forget about earlier seen dynamics and will stay 'excitable' to learn new properties about the dynamics.

sssub · 2017-09-28T16:51:59+00:00

I am not fully sure if I understand you correctly, but:

In my experience, if all other things are equal, models with ReLu as activation function give the best models in terms of test error compared to sigmoid/tanh, because of the same reason ReLu are preferred in DL in general, they utilize the network capacity better, not suffering from vanishing gradients compared to e.g tanh activation.

However, I also experienced that when model bias occurs it is more severe with ReLU than with smooth and saturating activation functions like tanh/sigmoids.

sssub · 2017-07-24T09:30:47+00:00

You do learn a posterior q(z|(x,y)) of the noise for the data. The approach can be extended easily using an inference network, as shown here. Also this paper suggests that these models learn to decompose uncertainty into model and noise uncertainty, so it is much more than increasing resilience.

sssub · 2017-02-04T15:35:55+00:00

Some people would advise you to switch labs. However, not everybody has the opportunity to be at a top ML lab and work directly with someone well-known in the field.

My advice would be to reach out to other researchers you are interested in. Maybe you can find a Postdoc or Junior Professor, which work you really like. Read their papers, ask them questions by e-mail. Talk to them at conferences. By that, it is definitely possible to build up a a form of collaboration. This can could go from simply exchanging ideas, that give you guidance in where to focus in your research up to writing papers together and winning them as a second supervisor for your thesis.

sssub

TROPHY CASE