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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

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Showing 8976–9000 of 9051 papers

TitleStatusHype
Embedding Cultural Diversity in Prototype-based Recommender Systems—0
Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation—0
Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy—0
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class—0
Test-Time Intensity Consistency Adaptation for Shadow Detection—0
Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network—0
A database for face presentation attack using wax figure faces—0
Interactions and migration rescuing ecological diversity—0
Emergent competition shapes the ecological properties of multi-trophic ecosystems—0
Emergent cooperative behavior in transient compartments—0
Emergent memory in cell signaling: Persistent adaptive dynamics in cascades can arise from the diversity of relaxation time-scales—0
Emergent properties with repeated examples—0
Emerging Diversity in a Population of Evolving Intransitive Dice—0
EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models—0
Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments—0
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Co-Learning Bayesian Optimization—0
Emotion Embeddings x2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets—0
EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning—0
Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning—0
EmpHi: Generating Empathetic Responses with Human-like Intents—0
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence—0
Text2Immersion: Generative Immersive Scene with 3D Gaussians—0
Empirical evidence of Large Language Model's influence on human spoken communication—0
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