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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

Showing 121130 of 9051 papers

TitleStatusHype
Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs0
GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure0
On the Performance of Pinching-Antenna Systems (PASS) with Orthogonal and Non-Orthogonal Multiple Access0
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon0
Bregman Centroid Guided Cross-Entropy Method0
Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm0
An Empirical Study of Group Conformity in Multi-Agent Systems0
Trajectory First: A Curriculum for Discovering Diverse Policies0
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-TuningCode5
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?0
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