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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Though deep learning has pushed the boundaries of classification forward, inrecent years hints of the limits of standard classification have begun toemerge. Problems such as fooling, adding new classes over time, and the need toretrain learning models only for small changes to the original problem allpoint to a potential shortcoming in the classic classification regime, where acomprehensive a priori knowledge of the possible classes or concepts iscritical. Without such knowledge, classifiers misjudge the limits of theirknowledge and overgeneralization therefore becomes a serious obstacle toconsistent performance. In response to these challenges, this paper extends theclassic regime by reframing classification instead with the assumption thatconcepts present in the training set are only a sample of the hypotheticalfinal set of concepts. To bring learning models into this new paradigm, a novelelaboration of standard architectures called the competitive overcompleteoutput layer (COOL) neural network is introduced. Experiments demonstrate theeffectiveness of COOL by applying it to fooling, separable concept learning,one-class neural networks, and standard classification benchmarks. The resultssuggest that, unlike conventional classifiers, the amount of generalization inCOOL networks can be tuned to match the problem.

Fitted Learning: Models with Awareness of their Limits