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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
Find Any Part in 3DCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Learning General-Purpose Biomedical Volume Representations using Randomized SynthesisCode2
EgoMimic: Scaling Imitation Learning via Egocentric VideoCode2
Flaming-hot Initiation with Regular Execution Sampling for Large Language ModelsCode2
MiniPLM: Knowledge Distillation for Pre-Training Language ModelsCode2
PUMA: Empowering Unified MLLM with Multi-granular Visual GenerationCode2
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language ModelsCode2
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven InteractionsCode2
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