Typically, you would like to understand how the network arrives to specific decisions.
The main insight of Google’s research is to not see the different interpretability techniques in isolation but as composable building blocks of larger models that help understand the behavior of neural networks.
For instance, feature visualization is a very effective technique to understand the information processed by individual neurons but fails to correlate that insight with the overall decision made by the neural network.
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.
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.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. 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.Despite the later success of the neural network, traditional von Neumann architecture took over the computing scene, and neural research was left behind.Ironically, John von Neumann himself suggested the imitation of neural functions by using telegraph relays or vacuum tubes.Unfortunately for him, the first attempt to do so failed.In 1959, Bernard Widrow and Marcian Hoff of Stanford developed models called "ADALINE" and "MADALINE." In a typical display of Stanford's love for acronymns, the names come from their use of Multiple ADAptive LINear Elements..pass_color_to_child_links a.u-inline.u-margin-left--xs.u-margin-right--sm.u-padding-left--xs.u-padding-right--xs.u-relative.u-absolute.u-absolute--center.u-width--100.u-flex-inline.u-flex-align-self--center.u-flex-justify--between.u-serif-font-main--regular.js-wf-loaded .u-serif-font-main--regular.amp-page .u-serif-font-main--regular.u-border-radius--ellipse.u-hover-bg--black-transparent.web_page .u-hover-bg--black-transparent:hover. Content Header .feed_item_answer_user.js-wf-loaded .