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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 26712680 of 9051 papers

TitleStatusHype
Boundary Matters: A Bi-Level Active Finetuning Framework0
Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookupCode0
SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different LanguagesCode0
Dyadic Interaction Modeling for Social Behavior GenerationCode1
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation0
Pantypes: Diverse Representatives for Self-Explainable ModelsCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space ModelsCode2
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation0
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models0
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