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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 581590 of 9051 papers

TitleStatusHype
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
DivClust: Controlling Diversity in Deep ClusteringCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense KnowledgeCode1
DISCO: Distilling Counterfactuals with Large Language ModelsCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Bayesian Adversarial Human Motion SynthesisCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed UsersCode1
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