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

With the surge in the number of hyperparameters and training times of modernmachine learning models, hyperparameter tuning is becoming increasinglyexpensive. However, after assessing 40 tuning methods systematically, we findthat each faces certain limitations. In particular, methods that speed uptuning via knowledge transfer typically require the final performance ofhyperparameters and do not focus on low-fidelity information. As we demonstrateempirically, this common practice is suboptimal and can incur an unnecessaryuse of resources. It is more cost-efficient to instead leverage low-fidelitytuning observations to measure inter-task similarity and transfer knowledgefrom existing to new tasks accordingly. However, performing multi-fidelitytuning comes with its own challenges in the transfer setting: the noise inadditional observations and the need for performance forecasting. Therefore, wepropose and conduct a thorough analysis of a multi-task multi-fidelity Bayesianoptimization framework, which leads to the best instantiation--amortizedauto-tuning (AT2). We further present an offline-computed 27-taskhyperparameter recommendation (HyperRec) database to serve the community.Extensive experiments on HyperRec and other real-world databases illustrate theeffectiveness of our AT2 method.

Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendation