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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 731–740 of 9051 papers

TitleStatusHype
Seasonal Station-Keeping of Short Duration High Altitude Balloons using Deep Reinforcement Learning—0
Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types—0
Leveraging band diversity for feature selection in EO data—0
Multi-Agent Reinforcement Learning with Focal Diversity OptimizationCode0
Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance—0
TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi—0
When One LLM Drools, Multi-LLM Collaboration Rules—0
UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generation—0
Pre-Optimized Irregular Arrays versus Moveable Antennas in Multi-User MIMO Systems—0
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation—0
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