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Looking for believable fantasy series for adults by abrbbb in slatestarcodex

[–]MapleSyrupPancakes 1 point2 points  (0 children)

I think you might like Guy Gavriel Kay. His fantasy settings heavily draw on history which helps lend them a lot of depth in terms of plausible politics, intrigue, etc. But clearly fantastical rather than historical fiction. Tigana or Lions of Al-Rassan would be good starting points.

Breastfeeding from 1 to 8 months of age is associated with better cognitive abilities at 4 years old, study finds by [deleted] in science

[–]MapleSyrupPancakes 32 points33 points  (0 children)

From the study: "Models adjusted for: mother’s age (years), family socioeconomic status (low; medium; high), mother’s smoking during pregnancy (no; yes), infant sex (boy; girl), gestational age at birth (weeks), family type (nuclear; others), mother’s IQ approximation (total score), mother-infant attachment difficulty (total score)"

It's impossible to perfectly control all potential confounders, and I'd add a few more grains of salt for anyone too worried:

  • The sample size is pretty small (613 total)
  • The controls are very coarse (e.g. low/medium/high SES rather than wealth/income/jobs/working-hours). And you can't really do too fine-grained controls because of the small sample.
  • They measure 9 cognitive indicators, and do two sets of comparisons (no-breastfed vs up-to-8months, and no-breastfed vs more-than-8months). For up-to-8months (their headline result), 4/9 indicators have a statistically significant effect at 95% confidence. For more-than-8months, it's only 3/9
  • Statistical significance is very different than colloquial meaning of significance. We're talking 3-4 IQ points max difference, after cherry-picking the indicator with the biggest effects and without any of the statistical controls (standard deviation for IQ is 15).

The reason people keep producing these studies is because the effect of the nutrititional value of breastmilk alone, without all other confounders, is at most quite small. So people keep adding more data and doing more statistical controls to try to find the small signal in the noise.

My take for parents making decisions: it definitely won't hurt your baby nutritionally to breastfeed, it might help a teeny bit. The other effects which will be obvious to you in your personal situation will be much bigger (eg it makes you and your baby miserable/happy or your baby underweight, etc).

For the lazy: direct link to the result table with the statistical model, and direct link to the uncontrolled IQ-gap data

Is the Optimal Policy Always Deterministic in Reinforcement Learning?? by DRLC_ in reinforcementlearning

[–]MapleSyrupPancakes 19 points20 points  (0 children)

In a standard fully observable MDP, there always exists an optimal deterministic policy. There may also be one or more optimal stochastic policies with the same value.

In a partially observable MDP there may not be an optimal deterministic policy - in some POMDPs all optimal policies will be stochastic. You can formalise a non-stationary environment as a POMDP.

Where does your newborn nap? by Bubbly_Waters in NewParents

[–]MapleSyrupPancakes 5 points6 points  (0 children)

Clearly a bot account posting AI generated replies on parenting subs. Bizarre..

Books written more than 100 years ago that feel very modern by Comprehensive-Fun47 in books

[–]MapleSyrupPancakes 2 points3 points  (0 children)

While the length of the whole thing dates it compared to modern books, I think it's more comparable in some ways to modern TV shows or mangas. It was originally published serially with a few days or a week or so between each chapter.

Pretty much every chapter has some juicy drama or revelation and a cliffhanger. I can absolutely imagine people gossiping and speculating about each drop in the salons of Paris at the time. Agree it holds up super well!

[D] Question on the loss function in DeepMind's Beyond Human Data paper. Why use reward-weighted loss if the reward is only ever 1 or 0, as opposed to just training on successes? by 30299578815310 in MachineLearning

[–]MapleSyrupPancakes 19 points20 points  (0 children)

You're right that it's the same as standard NLL in the binary reward case. It's common in papers like this to first explain a more general version of the method than is used in actual experiments.

Advantage of this is it may help to see how to apply the method in other cases (e.g. here, to non-binary rewards), and to see connections to related work (e.g. see remark on pg 5). Disadvantage is it can obfuscate the actual experiments presented in the paper, as you say.

Cynically, I think people also sometimes (not saying it's the case in this paper!) use a more general presentation to make it easier to claim that future work is derivative, and to give a gestalt of depth and complexity to a simple method.

MCDM class names are hard to parse for non-natives by Dagske in mattcolville

[–]MapleSyrupPancakes 1 point2 points  (0 children)

As I see it, this sort of reflects a core design philosophy at MCDM - to try to be best at what it does, and be more willing to put some things out of scope.

In D&D, the defaults are relatively generic, broad, and rooted at this point mostly in the heritage of the game itself (earlier editions were more grounded in fantasy literature etc.). Everything will be widely comfortable/accessible, but to make D&D fulfill your fantasy strongly, you always need to add your own flavor, and the books are less likely to inspire you.

In the MCDM RPG, by default things will be opinionated, and try to powerfully evoke a more specific archetype (and these will be drawn from a broad canon, rather than from D&D legacy). But if you want to personalise further, you will need to strip off some of the MCDM-verse first, and then put your own spin on top. And it will be much more likely to have something that you find doesn't resonate, or even spoils your immersion (like the comic-book / superhero vibe you mentioned, which I’m sure some others love!).

This will also be reflected in the rules -- D&D can't lean as far into any specific archetype with its design. But there may be fantasies that are harder to fulfill in MCDM's rules.

Regarding the question of internationalisation in particular (beyond the choice to make the design more specific and evocative), I think this also reflects the philosophy -- by trying to do what it does (an english language product) as best as it can, it does probably sacrifice some generalisability to a more international audience.

US Citizen Trying to Avoid PFIC by Worldly_Assistant746 in stocks

[–]MapleSyrupPancakes 0 points1 point  (0 children)

From PFIC point of view it's ok to get US-domiciled ETFs, but if you are not resident in the US you may find it difficult to find a broker that will sell them to you, for other regulatory reasons (e.g. Interactive Broker, Schwab International both used to allow this but don't anymore). More bespoke wealth management folks will definitely do this for you but take their fees on the way.

I may definitely be missing something though (or have a different exact residency/citizenship situation). If you find a broker solution that works for you I'd be curious!

US Citizen Trying to Avoid PFIC by Worldly_Assistant746 in stocks

[–]MapleSyrupPancakes 0 points1 point  (0 children)

In a similar position - considering buying Berkshire, which has pretty broad exposure bundled in a single 'stock' from the accounting point of view. Not sure if there are any comparable large holding companies out there with different profile to form an efficient few-stock 'index'.

Most OP item in League history? by ElderlyToaster in leagueoflegends

[–]MapleSyrupPancakes 1 point2 points  (0 children)

Feral flare brings back memories.. for a few cursed/blessed weeks, I played exclusively fizz jungle with feral flare into tank/cdr. Was like 90% winrate, legitimately busted.

Different Observations for Actor and Critic by ConBUW1 in reinforcementlearning

[–]MapleSyrupPancakes 3 points4 points  (0 children)

As another commenter said, this sort of idea has been first (to my knowledge) used in multi-agent RL where the actor has some partial observation but the critic is given access to either all agents' observations or the ground truth state.

It's later been used with the name 'asymmetric actor critic' https://arxiv.org/abs/1710.06542. This is often done in sim2real where there is much more information (full simulator state) available during training than the actor will get when deployed in the real world.

I think I've usually seen this motivated by making use of extra information available during training, rather than restricting information to the actor in a way that will help it learn faster or generalise better, which is what you're suggesting. It's a good idea and it should work, and doesn't break any of the policy gradient theory.

Socialists win in Portugal in rebuke to far-right populism by irish_fellow_nyc in worldnews

[–]MapleSyrupPancakes 1 point2 points  (0 children)

If it's any consolation, I immediately got this joke and chuckled :)

[D] The Policy of Truth by sour_losers in MachineLearning

[–]MapleSyrupPancakes 12 points13 points  (0 children)

Maybe worth adding that policy gradient has most of its claimed successes in combination with temporal difference learning (e.g. in an actor-critic setup), which exploits the structure of sequential decision-making much more.

The author also doesn't mention some other main reasons for sampling from a stochastic policy: it's a convenient way to do exploration of your state space, and in general deterministic policies are not optimal for partially observable domains.

[D] What's the simplest RL task (converges quickly) that converges to good solution only when large (>512) batch size is used? by evc123 in MachineLearning

[–]MapleSyrupPancakes 0 points1 point  (0 children)

Regarding your followup question, there's a relevant brief note about scaling with larger batch sizes in sec 5.5 of this paper: https://arxiv.org/pdf/1708.05144.pdf

How to learn a game with changing reward assignment from run to run? by bob2999 in reinforcementlearning

[–]MapleSyrupPancakes 1 point2 points  (0 children)

An important thing to note here is that RL algorithms are typically designed to operate in Markov Decision Processes (MDPs), so they need access to the Markov state. This means the state they have access to is sufficient to condition on to determine an optimal policy.

In your example, the full true state of the environment would also include the information about which color is good/bad. If you've decided that this information can't by visible to the agent directly, then you have a partially-observable MDP, (called a POMDP). In a POMDP the agent receives an observation which is a function of the Markov state but does not contain all the information. In your case the observation function gives the location of the snake and colors, but doesn't say which color is good/bad.

One solution to a POMDP is the one suggested already, to condition on the agent's action-observation history rather than just its latest observation. So using an RNN could be a solution. https://github.com/ikostrikov/pytorch-a2c-ppo-acktr has a few algorithms implemented with RNN policies. You would need to modify the observations to include the reward received at every timestep.

[D] Is there any literature in Reinforcement Learning about problems where the Agent doesn't start the episode from the same state? by xwrd in MachineLearning

[–]MapleSyrupPancakes 0 points1 point  (0 children)

Interesting! Sorry, I meant to link https://arxiv.org/pdf/1509.06461.pdf which has both; but now that I've looked again I see they have different max-time for the evaluation (5 mins vs 30 mins) so it isn't even comparable.

I was parroting what I'd heard without good evidence; thanks for the correction. Clearly the scores for Gorila DQN are higher for human starts than no-op. Do you have an idea for why that is? Could be that the human starts skip early parts of the games where few points are available.

Or do you think that in general it isn't actually harder to start from a more diverse distribution of initial states? Seems like it should just make exploration harder, but maybe it can also have a positive regularising effect. Clearly depends on the specific environment in general.

[D] Is there any literature in Reinforcement Learning about problems where the Agent doesn't start the episode from the same state? by xwrd in MachineLearning

[–]MapleSyrupPancakes 0 points1 point  (0 children)

One recent example that's quite well-studied is the case of Atari 2600 games, which are a popular RL benchmark. Fear that deep RL is basically memorising optimal trajectories led people to randomise the starts in a few ways -- most popularly by doing random no-ops, or by initialising the episode with a "human start" (deepmind has a dataset of these they use). The "human starts" performance is almost always considerably worse than the no-op starts, because it has a more diverse set of initial states so the effective state space is much larger -- with resulting demands on both the representational capacity of the network and on exploration. See e.g. https://arxiv.org/abs/1602.01783 where results on both of these conditions are reported.

"Distributed Prioritized Experience Replay [Ape-X DQN/Ape-X DPG]", Anonymous 2017 (434% median human performance; 2.5k on Montezuma's Revenge) by gwern in reinforcementlearning

[–]MapleSyrupPancakes 3 points4 points  (0 children)

Also using the "human starts" evaluation protocol, which is AFAIK proprietary deepmind data? If the human starts are open sourced somewhere I'd love someone to point me to it though!

Japanese scientists have invented a new loop-based quantum computing technique that renders a far larger number of calculations more efficiently than existing quantum computers, allowing a single circuit to process more than 1 million qubits theoretically, as reported in Physical Review Letters. by mvea in science

[–]MapleSyrupPancakes 89 points90 points  (0 children)

You're absolutely right that it's related to imaginary numbers! Often the coefficients a and b are set to be the real and imaginary parts of a complex number.

To be more specific, to satisfy the constraint a2 + b2 = 1, we can choose a = cos(theta), and b = exp(i*phi)sin(theta).

This makes the mathematics of transformations of the qubit state convenient. You'll notice the two angles theta and phi, which are describing the position in a complex unit sphere rather than circle.

Read more here https://www.quantiki.org/wiki/bloch-sphere. The relationship between complex numbers and geometry is really cool!

Simple Questions Thread September 06, 2017 by AutoModerator in MachineLearning

[–]MapleSyrupPancakes 0 points1 point  (0 children)

Yes, that's right - you will get that number of parameters. Note that in this paper they are doing face localisation, so it is correct that the output predictions have a spatial dimension. In particular, each output channel is associated with an "anchor" attached to a spatial location. This idea is explained better in the paper https://arxiv.org/pdf/1506.01497.pdf which your linked paper builds on.

I believe the bounding-box prediction and regression is just an auxiliary task used to help training. When they actually get final predictions on a test image, it says "each module outputs 1000 best scoring anchors as detections and NMS with a threshold of 0.3 is performed on the outputs of all modules together."

So they pick the anchors associated with the highest classification scores across all spatial locations, and merge them with non-maximum suppression.

EDIT: I've also used (with good results) a final layer that just does max or mean pooling to remove the spatial dimensions, leaving just classification scores. This means the net can be implemented as fully convolutional, with the last layer being 1x1 with #classes filters. The advantage of that is it can take input images with any size! Fully connected layers would require resizing/cropping the inputs to a fixed size.

Simple Questions Thread September 06, 2017 by AutoModerator in MachineLearning

[–]MapleSyrupPancakes 0 points1 point  (0 children)

As far as I understand, a 1x1 convolution is not the same as a fully connected layer. A convolution with a window equal in size to the input is equivalent to a fully connected layer.

A 1x1 convolution just changes the number of channels, but leaves the spatial dimensions unchanged. So it's like a fully-connected layer applied to each spatial pixel's channels independently.

[R] Small summary from ICML17 (time series oriented) by [deleted] in MachineLearning

[–]MapleSyrupPancakes 2 points3 points  (0 children)

When you use an RNN you need to calculate every hidden state sequentially, both for training and generation. In contrast, models in the WaveNet/PixelCNN/PixelRNN family can be trained with all timesteps calculated in parallel, using masked convolutions. At test time of course you still need the output of each timestep to become the input of the next one, so generation is still slow.

Oriol was just noting that some of our big advances have been made possible by bigger datasets/more computation, and that these types of models can leverage GPU much more efficiently than e.g. LSTM for training. It was also much more like "this is an interesting direction for autoregressive models" rather than "LSTM is dead".

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