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JohnLeM
Posts: 380
Joined: September 16th, 2008, 7:15 pm

Re: If you are bored with Deep Networks

December 19th, 2019, 9:05 pm

Test code 
// Interval strategy on (O,inf):
//
// 1. Truncation to large finite T
//  2. Transform (0,inf) to (0,1)
//
// 2. tends to give more accurate rounded results.
//
// (C) Datasim Education BV 2020
//


#include "LinearSystemGradientDescent.hpp"
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/io.hpp>
namespace ublas = boost::numeric::ublas;

#include <boost/numeric/odeint.hpp>
namespace Bode = boost::numeric::odeint;

int main()
{
 
 // 2X2 matrices
 //
 // A1 == symmetric and positive definite (pd)
 // A2 == symmetric and NOT positive definite (pd)
 // A3 == NOT symmetric and positive definite(pd)
 // A4 == NOT symmetric and NOT positive definite(pd)
 std::size_t r = 2; std::size_t c = 2;

 ublas::matrix<double> A1(r, c);
 A1(0, 0) = 2; A1(0, 1) = 1; A1(1, 0) = 1; A1(1, 1) = 2;
 
 
 ublas::vector<double> b1(r); 
 b1[0] = 4;  b1[1] = 5;
 

 
 // ODE Solver, x = (1 2) is solution in all cases
 ublas::vector<double> x(r); x[0] = x[1] = 0.0;
 ublas::vector<double> x2(r);  x2[0] = x2[1] = 0.0;

 // Integrate on [L,T]
 // EXX. Try T = 0.1 0.25, 0.5, 0.75, 0.95. 0.9999 etc.
 double L = 0.0; double T = 0.99584959825795;
 double dt = 1.0e-5;


 LinearSystemGradientDescentOde ode(A1, b1);

 // Cash Karp (Ford Cortina)
 std::size_t steps = Bode::integrate(ode, x, L, T, dt);

 std::cout << "Number of steps " << steps << '\n';
 std::cout << "Solution " << x << '\n';

 // BS upmarket model
 Bode::bulirsch_stoer<state_type, value_type> bsStepper;
 
 std::size_t steps3 = Bode::integrate_adaptive(bsStepper, ode, x2, L, T, dt);

 std::cout << "Number of steps, Bulirsch-Stoer " << steps3 << '\n';
 std::cout << "Solution II " << x2 << '\n';
 

 return 0;
}
Thanks for the sharing the boost::odeint example, I was not aware of this boost library.
By the way, reading the posts, I am not sure to understand your point. Is not SGD an optimization method, as all those present in boost::odeint ? Isn'it this discussion equivalent to compare for instance a Godunov scheme to a glimm one for numerical analysis of PDEs ?
 
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Cuchulainn
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Re: If you are bored with Deep Networks

December 21st, 2019, 2:10 pm

By the way, reading the posts, I am not sure to understand your point. Is not SGD an optimization method, as all those present in boost::odeint ? 

Boost odeint is a generic ODE solver...(?) It can be used to solve optimisation problems in dynamical systems as my example above shows.
The authors of odeint do have examples of Hamiltonian systems which is a special case of dynamical systems.

Isn'it this discussion equivalent to compare for instance a Godunov scheme to a glimm one for numerical analysis of PDEs ?

In which aspects is it equivalent? AFAIK Godunov is for conservation laws(?) I don't see much overlap.
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JohnLeM
Posts: 380
Joined: September 16th, 2008, 7:15 pm

Re: If you are bored with Deep Networks

December 23rd, 2019, 8:08 am

By the way, reading the posts, I am not sure to understand your point. Is not SGD an optimization method, as all those present in boost::odeint ? 

Boost odeint is a generic ODE solver...(?) It can be used to solve optimisation problems in dynamical systems as my example above shows.
The authors of odeint do have examples of Hamiltonian systems which is a special case of dynamical systems.

Isn'it this discussion equivalent to compare for instance a Godunov scheme to a glimm one for numerical analysis of PDEs ?

In which aspects is it equivalent? AFAIK Godunov is for conservation laws(?) I don't see much overlap.
You are right, Godunov scheme, as Glimm schemes, are indeed numerical schemes designed to solve both a particular problem, that are conservation laws, described by hamiltonians. For me, the connection with ODE minimization problem is that both methods basically uses gradient descent algorithms, that are energy-minimization based, more precisely entropic ones, to design an ODE, that is a dynamical system, consistent with the conservation law under study. Both are optimization problems solving. The difference being that

- Godunov schemes are gradient-based minimization methods, as are a bunch of other finite-difference type methods. They try to minimize the total system entropy.
- Glimm schemes are also gradient minimization methods, but they use random selection to achieve that. 

To try clarifying the connection : Glimm schemes are Stochastic gradient Descent based algorithms, Godunov corresponds to a particular finite-difference based minimization method. I guess that I could use boost ODEINT to describe both resulting numerical schemes ?
 
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Cuchulainn
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Re: If you are bored with Deep Networks

January 8th, 2020, 1:38 pm

A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder

https://arxiv.org/pdf/1906.10015.pdf
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ISayMoo
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Posts: 2337
Joined: September 30th, 2015, 8:30 pm

Re: If you are bored with Deep Networks

January 13th, 2020, 3:44 pm

Learning guarantees for Stochastic Gradient Descent

In a wide range of problems, SGD is superior to GD.
 
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Cuchulainn
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Re: If you are bored with Deep Networks

January 14th, 2020, 3:06 pm

Image
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Cuchulainn
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Re: If you are bored with Deep Networks

January 23rd, 2020, 8:13 pm

Despite some evidence for top-down connections in the brain, there does not appear to be a global objective that is optimized by backpropagating error signals. Instead, the biological brain is highly modular and learns predominantly based on local information. 

https://arxiv.org/pdf/1905.11786.pdf

In addition to lacking a natural counterpart, the supervised training of neural networks with end-to-end backpropagation suffers from practical disadvantages as well. Supervised learning requires labeled inputs, which are expensive to obtain. As a result, it is not applicable to the majority of available data, and suffers from a higher risk of overfitting, as the number of parameters required for a deep model often exceeds the number of labeled datapoints at hand. At the same time, end-to-end backpropagation creates a substantial memory overhead in a naïve implementation, as the entire computational graph, including all parameters, activations and gradients, needs to fit in a processing unit’s working memory. Current approaches to prevent this require either the recomputation of intermediate outputs [Salimans and Bulatov, 2017] or expensive reversible layers [Jacobsen et al., 2018]. This inhibits the application of deep learning models to high-dimensional input data that surpass current memory constraints. This problem is perpetuated as end-to-end training does not allow for an exact way of asynchronously optimizing individual layers [Jaderberg et al., 2017]. In a globally optimized network, every layer needs to wait for its predecessors to provide its inputs, as well as for its successors to provide gradients.
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Cuchulainn
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Re: If you are bored with Deep Networks

July 12th, 2020, 1:14 pm

Algorithmic AI Decolonianism

https://arxiv.org/pdf/2007.04068.pdf

Artificial Intelligence (AI) is viewed as amongst the technological advances that will reshape modern societies and their relations

That's also what they said in 1965.
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ISayMoo
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Joined: September 30th, 2015, 8:30 pm

Re: If you are bored with Deep Networks

July 13th, 2020, 8:55 am

Do you mean colonialism or AI?
 
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Cuchulainn
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Re: If you are bored with Deep Networks

July 13th, 2020, 9:25 am

I have read the half of that paper; it's all gobbelly-gook. 
“It's a beautiful thing, the destruction of words.”   
Step over the gap, not into it. Watch the space between platform and train.
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Cuchulainn
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Re: If you are bored with Deep Networks

August 6th, 2020, 12:10 pm

So, AlphaGo is using reinforcement learning. And reinforcement learning works for games; it works for situations where you have a small number of discrete actions, and it works because it requires many, many, many trials to run anything complex. AlphaGo Zero [the latest version of AlphaGo] has played millions of games over the course of a few days or few weeks, which is possibly more than humanity has played at a master level since Go was invented thousands of years ago. This is possible because Go is a very simple environment and you can simulate it at thousands of frames per second on multiple computers. [...] But this doesn’t work in the real world because you cannot run the real world faster than real time.

The only way to get out of this is to have machines that can build, through learning, their own internal models of the world, so they can simulate the world faster than real time. The crucial piece of science and technology we don’t have is how we get machines to build models of the world.
The example I use is when a person learns to drive, they have a model of world that lets them realize that if they get off the road or run into a tree, something bad is going to happen, and it’s not a good idea. We have a good enough model of the whole system that even when we start driving, we know we need to keep the car on the street, and not run off a cliff or into a tree. But if you use a pure reinforcement learning technique, and train a system to drive a car with a simulator, it’s going to have to crash into a tree 40,000 times before it figures out it’s a bad idea. So claiming that somehow just reinforcement learning is going to be the key to intelligence is wrong.
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