Consider the following move and commentary dataset for a chess game –

1,e4,e5,Both players have moved their pawns to oppose each other 2,Nf3,d6,White has moved his Knight while Black moved a pawn one square 3,d4,Bg4,White moved a pawn two squares while Black moved his Bishop 4 squares. 

Full example here – https://www.family-games-treasurehouse.com/sample_chess_game.html

If I can create such a dataset, what is the state of the art I can use to train a commentary generation model? GPT-2 might be a starting point but it gives me no control over entities, actions.

Curious if us humans have any ideas to go about this.

vulnerability to adversarial attacks is pretty well known now. I think it’s better to show improvement in robustness over improvements in accuracy now. 1% improvement under attack conditions is much more significant than 1% improvement in accuracy.

Edit: context.

Speaking from an academic point of view, for newly proposed algorithms.

Hi guys,

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I’m currently working on a ML project that considers (mostly) quantitative figures, i.e. a ‘classical’ ML problem with underlying data coming from a .csv file.

​

The problem setting is the following: I want to predict if workers are missing their shifts or not. For this problem I have a shift plan available, among other datasets. The plan tracks the date, shift durations and other very obvious data and of course if the workers were present for the respective shift or not (=Target Variable). I already did some EDA on the shift plan and incorporated some features for the classifier that were referring to the last shift. For example: For the shift in question, I incorporated a Feature that is documenting the number of consecutive shifts for the respective worker that he/she was present. Or how many consecutive shifts was the worker absent. For this, I just shifted the calculated column down. The following example might help:

|Date|ID|PRESENT|

​

|2019-01-01|1|YES|

|2019-01-01|2|YES|

|2019-01-03|1|NO|

|2019-01-05|2|YES|

|2019-01-06|2|YES|

​

becomes

​

|Date|ID|PRESENT|Consecutive_Shifts_Present|

​

|2019-01-01|1|YES|NaN|

|2019-01-01|2|YES|NaN|

|2019-01-03|1|NO|1|

|2019-01-05|2|YES|1|

|2019-01-06|2|YES|2|

​

​

I feel that this is a valid approach to incorporate historic information of the shifts/worker for the shift that needs to be predicted. If not, please tell me what I missed at this point and what approach I should rather consider.

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The actual problem now comes with other datasets that I want to join with the shift plan dataset. For example I have a dataset that tracks the assignments of the workers per shift that he/she has accomplished. Again, there are some quantitative figures recorded per assignment. Along the same lines as the shift dataset, I want to incorporate some historic shift information of the worker for the shift that needs to be predicted. Therefore, I was trying to group the assignments per worker on a shift-base and calculate some quantitative figures (e.g. min, max, mean values). However, at this point I experienced the problem that there can be several consecutive not attended shifts for which there is no data from the assignment dataset. Therefore I don’t know how to incorporate the historic data. Consider the following example:

​

|Date|ID|PRESENT|Assignment_Measure|

​

|2019-01-01|1|YES|23|

|2019-01-03|1|NO|NaN|

|2019-01-05|1|YES|65|

|2019-01-06|1|NO|NaN|

|2019-01-07|1|NO|​NaN|

​

​

If I was trying to predict the shift at 2019-01-03 I could shift the column ‘Assignment_Measure’ one step size down like above. However, for the shift at 2019-01-07 I would still receive no information. A naive solution would be just to copy the values from the absent shift before but I think this is going to be problematic for the model as this would introduce many very similar rows.

​

Does any of you guys have an idea how to solve this problem? Or how this problem could be tackled?

Thanks a lot for your help and input!

I wrote a short post describing a basic idea I’ve thought about for a while – creating an objective for training a Neural Net where the task is imitating people. More specifically:

I propose training a model where the input is simply people interacting with their computer (just the browser for simplicity) as they normally do and the output is a prediction of the next action they will take.

I am specifically interested in any input on the ways people think this can and cannot work, alternatives, etc.

Also

For those who don’t think AGI is possible – I am not saying it definitely is (especially not immediately), just proposing a task to get closer to something that is realistically dosable.

I know we dont really have this yet. But if you were pushed to try explain how you think machine learning without innate priors would look like, what do you think?

My question is about what constitutes a perfect Variational Autoencoders.

I am a bit confused by Table 1 on the paper about IAF Variational Autoencoders (https://arxiv.org/abs/1606.04934) however my question is about Variational Autoencoders in general.

The table looks somewhat like below (numbers different to the one in the paper):

​

​

In the typical derivation of the Variational Autoencoder (VAE) we find that we get the optimal model when our approximate posterior q(z|x; theta) approaches the true posterior p(z|x) and the difference between our VLB and log p(x) is KL[q(z|x) || p(z|x)].

On table 1, it shows values of both the VLB and log p(x) for different VAE models. It is clear why the VLB is less than log p(x) because of the gap between the approximate and true posterior, however why are the true marginals log p(x) different for different models?

After you’ve closed the gap between the approximate and true posterior – shouldn’t you achieve the perfect model? so why is it the case that the true marginals differ for different VAE models?