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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 17711780 of 9051 papers

TitleStatusHype
Confidence-weighted integration of human and machine judgments for superior decision-makingCode0
A Hybrid Retrieval-Generation Neural Conversation ModelCode0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
Computing recommendations via a Knowledge Graph-aware AutoencoderCode0
A Simple Yet Effective Approach for Diversified Session-Based RecommendationCode0
A Block-Based Adaptive Decoupling Framework for Graph Neural NetworksCode0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
Improving Screening Processes via Calibrated Subset SelectionCode0
Active Learning for Abstractive Text SummarizationCode0
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