SOTAVerified

Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 16811690 of 9051 papers

TitleStatusHype
Alibaba Submission to the WMT20 Parallel Corpus Filtering Task0
Audio Generation with Multiple Conditional Diffusion Model0
A Two-stage Evolutionary Framework For Multi-objective Optimization0
Alibaba Submission to the WMT18 Parallel Corpus Filtering Task0
A DAFT Based Unified Waveform Design Framework for High-Mobility Communications0
A two-stage algorithm in evolutionary product unit neural networks for classification0
A Two-Layer Local Constrained Sparse Coding Method for Fine-Grained Visual Categorization0
An Algorithm for Multi-Attribute Diverse Matching0
A Tutorial On Intersectionality in Fair Rankings0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration0
Show:102550
← PrevPage 169 of 906Next →

No leaderboard results yet.