3 ms·
"Only 7 of them could be reproduced with reasonable effort. For these methods, it however turned out that 6 of them can often be outperformed with comparably si
by debt 7y ago
"Only 7 of them could be reproduced with reasonable effort. For these methods, it however turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques. "
Ha. I get a feeling there's alotta overpaid data scientists out there.
- baron_harkonnen 7y agoBasically when you see a RNN in industry, chances are somebody has made a huge and expensive mistake. And quite likely the person behind it thinks that they've applied cutting edge research to a real world problem (when they have done neither).
- quelltext 7y agoPlease elaborate. What's wrong with RNNs and what should be used instead?
- sdenton4 7y agoI met a dude once who was using LSTMs to predict HVAC failures for some sort of HVAC support firm. On some interrogation, it turned out that the input data was garbage, and the only way the model "worked" is if it was repeatedly overfit on only the last week's worth of data... comprising about 5k rows, iirc. This struck me as completely terrifying... it's not like the set of HVAC units was changing out every week. And one would hope that the historical data would help arrive at a general solution. My guess is they had some sort of church-meets-state problem in the test/train split, so that the short-term data allowed 'predicting' the test set. Dude was quite convinced that they were making HVAC history, though.
- rq1 7y agoOh yes elaborate please. I’m all hearing too. :)
- bigger_cheese 7y agoMy experience (working in industry) is machine learning projects tend to go something like this: Someone on the company board has just read a pop sci article about "machine learning" and now as a result the company has engaged some expensive consultants to advise us on "potential machine learning opportunities." The consultant spends maybe a day or two touring one of the company's plants and has only the most broad and high level view of how our industrial process actually works. The consultant then promises the world "machine learning is great, you can use it here, here and here for sure. It will save you millions..." It gets the board members excited. So the company picks some toy problem and gives the consultant and his team a six month contract to deliver a proof of concept. The deadline passes a solution is delivered - it is underwhelming. The contract is not extended. edit: All cynicism aside I appreciate the possibilities but for ML to succeed the developers really need to be embedded/integrated into the company have a good understanding of problem space and fundamentals. Not really something you can outsource easily and expect to succeed.