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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 20212030 of 9051 papers

TitleStatusHype
OTFS-NOMA System for MIMO Communication Networks with Spatial Diversity0
Factor-Conditioned Speaking-Style Captioning0
Manipulate-Anything: Automating Real-World Robots using Vision-Language Models0
UniGen: A Unified Framework for Textual Dataset Generation Using Large Language ModelsCode2
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
EmPO: Emotion Grounding for Empathetic Response Generation through Preference OptimizationCode0
RuBLiMP: Russian Benchmark of Linguistic Minimal PairsCode1
ACD-DE: An adaptive cluster division Differential Evolution for mitigating population diversity deficiencyCode0
Artificial Immune System of Secure Face Recognition Against Adversarial AttacksCode0
A Closer Look into Mixture-of-Experts in Large Language ModelsCode2
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