Research Paper On Neural Network

One of the challenging elements of any deep learning solution is to understand the knowledge and decisions made by deep neural networks.While the interpretation of decisions made by a neural networks has always been difficult, the issue has become a nightmare with the raise of deep learning and the proliferation of large scale neural networks that operate with multi-dimensional datasets.How does the new Google model for interpretability works specifically?

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Applying this rule still results in an error if the line before the weight is 0, although this will eventually correct itself.

If the error is conserved so that all of it is distributed to all of the weights than the error is eliminated.

Google research of deep neural network interpretability is not only a theoretical exercise.

The research group accompanied the paper with the release of Lucid, a neural network visualization library that allow developers to make the sort lucid feature visualizations that illustrate the decisions made by individual segments of a neural network.

In 1943, neurophysiologist Warren Mc Culloch and mathematician Walter Pitts wrote a paper on how neurons might work.

In order to describe how neurons in the brain might work, they modeled a simple neural network using electrical circuits.ADALINE was developed to recognize binary patterns so that if it was reading streaming bits from a phone line, it could predict the next bit.MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines.Knowing that neuron-12345 fired five times is relevant but not incredibly useful in the scale of the entire network.The research about understanding decisions in neural networks has focused on three main areas: feature visualization, attribution and dimensionality reduction.Google, in particular, has done a lot of work in the feature visualization space publishing some remarkable research and tools.Continuing their work in the space, Google researchers recently published a paper titled “The Building Blocks of Interpretability” that proposes some new ideas to understand how deep neural networks make decisions.While the system is as ancient as air traffic control systems, like air traffic control systems, it is still in commercial use.In 1962, Widrow & Hoff developed a learning procedure that examines the value before the weight adjusts it (i.e.0 or 1) according to the rule: Weight Change = (Pre-Weight line value) * (Error / (Number of Inputs)).It is based on the idea that while one active perceptron may have a big error, one can adjust the weight values to distribute it across the network, or at least to adjacent perceptrons.

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