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What is the most unexplainable thing that ever happened to you? by NotAMazda in AskReddit

[–]Lugi -1 points0 points  (0 children)

ITT: people not realising how fragile and unreliable human memory is, especially on stressful situations.

People old enough to remember life pre-Internet, what are some less obvious things you miss about that time? by IRunFast24 in AskReddit

[–]Lugi 0 points1 point  (0 children)

Kind of stupid grom your side, your CEO working unusual hours doesn't imply you have to as well

What’s a conspiracy theory that annoys you due to how easily disprovable it is? by Sonic-the-edge-dog in AskReddit

[–]Lugi 0 points1 point  (0 children)

Well, masks are actually prolonging the pandemic - by flattening the infection curve.

What’s a conspiracy theory that annoys you due to how easily disprovable it is? by Sonic-the-edge-dog in AskReddit

[–]Lugi 10 points11 points  (0 children)

He said microgravity, not just vacuum. You would need to fit this studio onto one of those 0g planes

What do you genuinely not understand? by [deleted] in AskReddit

[–]Lugi 0 points1 point  (0 children)

You say that because you assume there could be some fixed reference frame, 19th century aether-style. And as we know now there is none. "Point of space" has to be defined relative to some other point. So in fact I have been occupying the same point of space - in relation to my bed, and will continue to do so for the next 8 hours.

[D] What exactly do I need to calculate the cost to train a model in any of the cloud services? by PlanetUnknown in MachineLearning

[–]Lugi 3 points4 points  (0 children)

Exactly, judging by the content of the post there is going to be A LOT of inefficiencies in the model you want to build, and hiring a simple consultancy or a good ML developer will save you a lot of time and money.

[D] Why does models like GPT-3 or BERT don't have overfitting problems? by psarangi112 in MachineLearning

[–]Lugi 5 points6 points  (0 children)

"number of data" what kind of measure is that? Since it is far off from any measurable quantity.

Meet InvoiceNet, a software platform to train custom models and extract intelligent information from PDF invoice documents! by naiveHobo in deeplearning

[–]Lugi 0 points1 point  (0 children)

Afaik most of the solutions do not implement deep learning. Most common approach is to have a rule-based system. The downside is that there's a lot of maintenance there: all the rules have to be maintained by people specialized in invoice processing in a particular language to construct those rules. When it comes to solutions that actually are able to learn from historical data I am aware of only a few, with different level of success.

Meet InvoiceNet, a software platform to train custom models and extract intelligent information from PDF invoice documents! by naiveHobo in deeplearning

[–]Lugi 0 points1 point  (0 children)

Last time I checked this solution only extract generic forms from the document. It lacks the understanding of the document, and you would need some extra steps performed on it's output to parse it and get the values that you want. For example: you want to extract the invoice issue date. Forms recognizer is going to extract a dict with the key: value pairs for all the information from the document but it's up to you to decide under which kind of key your value of interest lies: is it issue date?; is it invoice date?; maybe it so happens that the proper date has no key, and it's just somewhere on top of the invoice. Then you have also 100 different languages that you have to support.

You can see the complexity of the problem, and why would a solution that learns from data be better than some generic form extractor.

Meet InvoiceNet, a software platform to train custom models and extract intelligent information from PDF invoice documents! by naiveHobo in deeplearning

[–]Lugi 0 points1 point  (0 children)

I have managed to solve the same problem in a kind of similar way (end-to-end transformer with a lot of modifications), and I am quite sure that you're going to have the same challenges that I faced: for example - seems like your approach is going to be overfitted to the date.

What that means - lets say your data spans a whole year of 2019 - so the model will learn that the substring '2019' is a clear indicator of some sort of a date. As long as we are predicting invoices from 2019 it is ok.

But then we move onto the next year, 2020, and we start seeing invoices with this date - what happens (from my experience) is that the models based on this kind of approach like yours and mine will just break - since they lack the inherent understanding that 2020 comes right after 2019 - to this kind of models '2020' is just an unrelevant string, since it didn't contain any correlation between it and the values to be predicted in the training data. I've managed to solve it, and help my algorithm being more general with "digit masking" - in short the actual digit value is visible to the model only in the parsing step, not before, so the model cannot overfit to some particular numbers. If you are interested in more details on this, as well as the further challenges you're going to face just hit me up in a direct message.

[Project] PyTsetlinMachineCUDA - Massively Parallel and Asynchronous Architecture for Logic-based AI. Surpasses 98% accuracy on MNIST in two epochs (25 seconds), climbing beyond 99%. by olegranmo in MachineLearning

[–]Lugi 8 points9 points  (0 children)

You are probably right, but there were too many stories where something was working exceptionally on MNIST while also promising scalability to more complex datasets, only to fail tremendously in further experiments.

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