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

Standard few-shot benchmarks are often built upon simplifying assumptions onthe query sets, which may not always hold in practice. In particular, for eachtask at testing time, the classes effectively present in the unlabeled queryset are known a priori, and correspond exactly to the set of classesrepresented in the labeled support set. We relax these assumptions and extendcurrent benchmarks, so that the query-set classes of a given task are unknown,but just belong to a much larger set of possible classes. Our setting could beviewed as an instance of the challenging yet practical problem of extremelyimbalanced K-way classification, K being much larger than the values typicallyused in standard benchmarks, and with potentially irrelevant supervision fromthe support set. Expectedly, our setting incurs drops in the performances ofstate-of-the-art methods. Motivated by these observations, we introduce aPrimAl Dual Minimum Description LEngth (PADDLE) formulation, which balancesdata-fitting accuracy and model complexity for a given few-shot task, undersupervision constraints from the support set. Our constrained MDL-likeobjective promotes competition among a large set of possible classes,preserving only effective classes that befit better the data of a few-shottask. It is hyperparameter free, and could be applied on top of any base-classtraining. Furthermore, we derive a fast block coordinate descent algorithm foroptimizing our objective, with convergence guarantee, and a linearcomputational complexity at each iteration. Comprehensive experiments over thestandard few-shot datasets and the more realistic and challenging i-Nat datasetshow highly competitive performances of our method, more so when the numbers ofpossible classes in the tasks increase. Our code is publicly available athttps://github.com/SegoleneMartin/PADDLE.

Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference