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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 2801–2810 of 9051 papers

TitleStatusHype
Diversity-Sensitive Conditional Generative Adversarial Networks—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
A Novel BiLevel Paradigm for Image-to-Image Translation—0
Diversity waves in collapse-driven population dynamics—0
Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task—0
Canny2Palm: Realistic and Controllable Palmprint Generation for Large-scale Pre-training—0
DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network—0
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
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