Google suggests this: https://github.com/KorfLab/StochHMMBTW what's a good C++ open-source for HMMs and BNs?
Google suggests this: https://github.com/KorfLab/StochHMMBTW what's a good C++ open-source for HMMs and BNs?
Thank you very much. Looks very good.Google suggests this: https://github.com/KorfLab/StochHMMBTW what's a good C++ open-source for HMMs and BNs?
Such libraries would benefit greatly by migrating them to Boost.Thank you very much. Looks very good.Google suggests this: https://github.com/KorfLab/StochHMMBTW what's a good C++ open-source for HMMs and BNs?
BUMPUnfortunately, the run-time performance of both ANN (3 hours) and FDM(20 days!) are disappointing. Must be chalking up a yuge electricity bill?Here is an interesting paper for friends relevant to this thread. An Artificial Neural Network Representation of the SABR Stochastic Volatility Model by William Mcghee.
With 'traditional' FDM popular consensus says it can be done in 15 minutes.
IMO, I am becoming more and more convinced that NN and PDE don't mix, really. Maybe that 'Eureka moment' will come...
For this kind of problem, I would expect Lewis/Papadoupolis FDM to be a good baseline.
https://arxiv.org/abs/1801.06141
BUMPI just read the paper per diagonals
Isn't the 475 hours for solving 300k FDM as the goal was to generate 3 millions volatilities (10 at a time)?
BUMPUpdate
Section 4.3
1. 1st and 2nd derivatives are not calculated using NN directly (by AD, whatever)... no reason given why. I suspect difficult to implement etc.
2. Instead author uses cubic splines and its derivatives. Something I have done some work on while back for FEM.
OK then, mathematically, each time you differentiate a spline it gets worser and worser (overshoot) as I discuss in mathematical detail and in numbers in the recent 2nd edition of my C++ book.
Two screen shoots coming up.
I'm not sure. It may be a more attractive tool for users if it's free of Boost dependencies. Some organisations ban Boost.Such libraries would benefit greatly by migrating them to Boost.Thank you very much. Looks very good.
Google suggests this: https://github.com/KorfLab/StochHMM
I suspect that Amazon made some pretty rookie errors in training their system. If they get a different error rate on black and white people, it's likely that their training dataset was unbalanced, e.g. had different number of white and black defendants. We don't know if they also trained it with photos of people who are not in the database of known suspects. It's very hard to say anything concrete about a closed-source system.Politicians fume after Amazon's face-recog AI fingers dozens of them as suspected crooks
https://www.theregister.co.uk/2018/07/2 ... ion_sucks/
Boost libraries are very professional. Not sure if a lot of github projects (e.g. StochHMM) are in the same league.I'm not sure. It may be a more attractive tool for users if it's free of Boost dependencies. Some organisations ban Boost.Such libraries would benefit greatly by migrating them to Boost.
Thank you very much. Looks very good.
Maybe the article was talking about an earlier version. Regarding CNTK, you need Windows 10 and it doesn't work in Windows 7.. And C# is also a great language. I am particularly interested in how the algorithms are implemented, i.e. not black box."TensorFlow also tries its best to run LSTM RNNs, but in vain."
That's not true.