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When a neural network solved a problem, it means it found the algebraic function it needs to solve the problem. > Once it found the algebraic function, there i
by imvetri 7y ago
When a neural network solved a problem, it means it found the algebraic function it needs to solve the problem.
> Once it found the algebraic function, there is no need to run the problem to the neural network. We can sideload the problem to a simple program that takes input and gives an output. This strategy can help to free up the GPU for next set of problem.
- ludwigschubert 7y agoHow would one extract an algebraic function from the network’s weights? Why would the function the network approximates necessarily be algebraic?
- Plough_Jogger 7y agoThe function would simply be a chain of multiplications and (typically) non-linear transformations. For classification problems, there is often a final non-linear transformation, like a softmax. Example with ReLu activation: Output_i = max((input_i * weight_i),0)
- jawarner 7y agoI don't understand. The example you gave is not an algebraic function.
- Plough_Jogger 7y agoYou are correct. It is possible to construct a linear neural network, where an algebraic function could be extracted, but in practice, almost all networks use non-linear activation functions. In the case of a linear network, the function would be a dot product between the input and the weights: 𝑥1𝑤1+𝑥2𝑤2+𝑥3𝑤3 ... for all inputs (xi) and weights (wi)