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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

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Showing 3431–3440 of 9051 papers

TitleStatusHype
Active Learning with Tabular Language Models—0
Absolute Ranking: An Essential Normalization for Benchmarking Optimization Algorithms—0
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
A Survey on Self-Evolution of Large Language Models—0
Contrastive Examples for Addressing the Tyranny of the Majority—0
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality—0
AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems—0
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization—0
Continuous Inference in Graphical Models with Polynomial Energies—0
A Survey on Backbones for Deep Video Action Recognition—0
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