English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this report we investigate fundamental requirements for the application ofclassifier patching on neural networks. Neural network patching is an approachfor adapting neural network models to handle concept drift in nonstationaryenvironments. Instead of creating or updating the existing network toaccommodate concept drift, neural network patching leverages the inner layersof the network as well as its output to learn a patch that enhances theclassification and corrects errors caused by the drift. It learns (i) apredictor that estimates whether the original network will misclassify aninstance, and (ii) a patching network that fixes the misclassification. Neuralnetwork patching is based on the idea that the original network can stillclassify a majority of instances well, and that the inner featurerepresentations encoded in the deep network aid the classifier to cope withunseen or changed inputs. In order to apply this kind of patching, we evaluatedifferent engagement layers and patch architectures in this report, and find aset of generally applicable heuristics, which aid in parametrizing the patchingprocedure.

Towards Neural Network Patching: Evaluating Engagement-Layers and Patch-Architectures