Neural Arithmetic Unit by reusing many small pre-trained networks
We propose a solution for evaluation of mathematical expression.However, instead of designing a single end-to-end model we propose a Lego bricks style architecture.In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick.More difficult or complex task can then be solved using a combination of these smaller network.In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations.These fundamental operations are then learned using simple feed forward neural networks.We then shows that different operations can be designed simply by reusing these smaller networks.As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product.This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers.Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.
We train many small networks each for a specific operation, these are then combined to perform complex operationsThis paper proposes to use neural networks to evaluate the mathematical expressions by designing 8 small building blocks for 8 fundamental operations, e.g., addition, subtraction, etc and then designing multi-digit multiplication and division using these small blocks.The paper proposes a method to design a NN based mathematical expression evaluation engine.