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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 5476–5500 of 9051 papers

TitleStatusHype
Smartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence—0
Tractable Diversity: Scalable Multiperspective Ontology Management via Standpoint EL—0
Measuring Sample Quality with Diffusions—0
Tradeoffs in Data Augmentation: An Empirical Study—0
Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election—0
Mechanics promotes coherence in heterogeneous active media—0
A Unified Statistical Model for Atmospheric Turbulence-Induced Fading in Orbital Angular Momentum Multiplexed FSO Systems—0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
Trading Off Diversity and Quality in Natural Language Generation—0
Medical visual question answering using joint self-supervised learning—0
A Unified Multi-Faceted Video Summarization System—0
MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding—0
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models—0
Meeting the 2020 Duolingo Challenge on a Shoestring—0
A unified framework based on graph consensus term for multi-view learning—0
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models—0
Melanoma Detection with Uncertainty Quantification—0
AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring—0
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation—0
Meme and Variations: A Computer Model of Cultural Evolution—0
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition—0
When can in-context learning generalize out of task distribution?—0
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory—0
When Costly Punishment Becomes Evolutionarily Beneficial—0
Augmenting Zero-Shot Detection Training with Image Labels—0
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