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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 45614570 of 9051 papers

TitleStatusHype
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Generative Modeling in Structural-Hankel Domain for Color Image InpaintingCode1
Secure Power Control for Downlink Cell-Free Massive MIMO With Passive Eavesdroppers0
Prosody-controllable spontaneous TTS with neural HMMs0
Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems0
Unsupervised User-Based Insider Threat Detection Using Bayesian Gaussian Mixture Models0
SciRepEval: A Multi-Format Benchmark for Scientific Document RepresentationsCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining0
Agent-Specific Deontic Modality Detection in Legal Language0
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