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

Casting machine learning as a type of search, we demonstrate that theproportion of problems that are favorable for a fixed algorithm is strictlybounded, such that no single algorithm can perform well over a large fractionof them. Our results explain why we must either continue to develop newlearning methods year after year or move towards highly parameterized modelsthat are both flexible and sensitive to their hyperparameters. We further givean upper bound on the expected performance for a search algorithm as a functionof the mutual information between the target and the information resource(e.g., training dataset), proving the importance of certain types of dependencefor machine learning. Lastly, we show that the expected per-query probabilityof success for an algorithm is mathematically equivalent to a single-queryprobability of success under a distribution (called a search strategy), andprove that the proportion of favorable strategies is also strictly bounded.Thus, whether one holds fixed the search algorithm and considers all possibleproblems or one fixes the search problem and looks at all possible searchstrategies, favorable matches are exceedingly rare. The forte (strength) of anyalgorithm is quantifiably restricted.

The Famine of Forte: Few Search Problems Greatly Favor Your Algorithm