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I have seen straight through estimator (STE) in many Neural Network related papers e.g. this and this. But I cannot understand the concept. I wonder if anyone could explain STE or refer me to a simple resource?
A straight through estimator is a way of estimating gradients for a threshold operation in a neural network. The threshold could be as simple as the following function,
As we can see, the derivative of this threshold function will 0 and during back-propagation, the network will not learn anything since it gets 0 gradients and the weights won't get updated.
The concept of a straight through estimator is that you set the incoming gradients to a threshold function equal to it's outgoing gradients, disregarding the derivative of the threshold function itself. This has been shown to perform well in the results (Figure 2) in this paper you have referenced.
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I've tried matlab, but unfortunately it is not threaded. I've also tried eigen and although it is threaded and scales quite well, the single thread performance is a little worse than Matlab.
How can I multiply a general large sparse * dense matrix in the fastest way possible on the CPU (not GPU).
Use both. For a single threaded environment, run matlab routines, for multi-threaded, go with eigen. And keep tabs on new developments because for highly competitive fields like these, any advice you get here will be out of date in a month.
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I want to understand the difference between Discrete and Continuous Solver in MATLAB Simulink.
Could anyone explain me this difference in a simple language with examples.
A state for the discrete solver consists only of values. A state for the continuous solver has values AND state derivatives.
In very plain terms, the continuous solvers are used (required) when you have continuous states, for example when using a continuous integrator or derivative block. Conversely, the discrete solvers are used/recommended when you have only discrete states, e.g. a discrete integrator or derivative block. The Simulink diagnostics will flag any solver compatibility issues.
For more details, have a look at the documentation, in particular this page on choosing a solver.
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I have a scalable quadratic programming problem which has around 50-1000 variables with linear constraints. I am trying to find an effective solver for this. The matlab qp solver can only solve to optimality for 100 variables for my problem. Will cplex be any better? Are there any other tools which I can use.
CPLEX will definitely be better. Everything depends on the environment you are working in. If it's not a problem for you to write a C++ program you can try COIN-OR projects, in particular Couenne http://www.coin-or.org/projects/Couenne.xml for nonlinear MIP's and IPOPT http://www.coin-or.org/projects/Ipopt.xml; for Python lovers they have Coopr, DilPy, GrumPy and other projects. You can also try GAMS https://www.gams.com/, it's great for any optimization problem. It's not a solver, more like modeling system with a large number of built in commercial and open-source solvers. It's free as long as your problem has no more than 50 variables and 50 constraints (as far as I remember).
You can try the Opti Toolbox: http://www.i2c2.aut.ac.nz/Wiki/OPTI/
It has an interface to different solvers which you can use in Matlab. (Precompiled mex files.) It can solve quadratic problems.
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I am going through Modelica libraries. I was wondering, that there are (or i can't found them) ODE's.
For example in the dynamic pipe model of the standard library.
Generally, the Modelica Standard Library contains many dynamic models - i.e. models that can be described with DAEs or ODEs (look for models using the der() operator).
The reason why you don't see any der() operators in DynamicPipe is that it inherits much of its functionality from several base classes - including Modelica.Fluid.Interfaces.PartialDistributedVolume where you will find the differential equation for mass and energy balances.
Modelica.Blocks.Continuous.FirstOrder is an example of a very simple ODE - a first-order low-pass filter.
You might want to consult the free online Modelica book "Modelica by Example". It shows many examples involving ordinary differential equations.
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What should I use for stock market prediction and why? comparison if you can please.
Udpated: I wanted to use it for stock market movement (up,down) for 1 day.Also,Thank you for your answer it halped
It's not easy to say you which ML algo will give you best perfomance. Especially if not to see which market you want to predict. I recommend you to implement different algorithms and try to train them, because in my practice changing of layers gave different results. SVM sometime was also flexible enough. Also try to implement and check how your training will work on trained and untrained data in order to have really good results. Also analyze how machine learning will work on more predictable sequences ( aka sin, cos, polinomials, randow walks)
Additional field of investigation can be some technical analisis additions: moving averages, stochastics, candle chart patterns, Fibonacci levels.
And finally in order to get money don't rely only on neural network or SVM but use them in conjunction with some trading strategy. For example you can use some trading strategy which has perfomance 30 % and use ML in order to rise perfomance to 60 %