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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 661670 of 9051 papers

TitleStatusHype
Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed UsersCode1
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
Diverse Video Generation using a Gaussian Process TriggerCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
Bootstrapping Referring Multi-Object TrackingCode1
BoostTree and BoostForest for Ensemble LearningCode1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
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