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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 3201–3225 of 9051 papers

TitleStatusHype
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels—0
Curating Grounded Synthetic Data with Global Perspectives for Equitable AI—0
CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques—0
A Unifying Information-theoretic Perspective on Evaluating Generative Models—0
A local continuum model of cell-cell adhesion—0
Cultural Incongruencies in Artificial Intelligence—0
A unified view of generative models for networks: models, methods, opportunities, and challenges—0
Cultural Evaluations of Vision-Language Models Have a Lot to Learn from Cultural Theory—0
A Load Balanced Recommendation Approach—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
Accelerated Image-Aware Generative Diffusion Modeling—0
Cultural Diversity and Its Impact on Governance—0
Semantic and Expressive Variation in Image Captions Across Languages—0
A Unified Statistical Model for Atmospheric Turbulence-Induced Fading in Orbital Angular Momentum Multiplexed FSO Systems—0
Cultivating DNN Diversity for Large Scale Video Labelling—0
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network—0
A Unified Multi-Faceted Video Summarization System—0
All You Need Is Sex for Diversity—0
Cuid: A new study of perceived image quality and its subjective assessment—0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation—0
A unified framework based on graph consensus term for multi-view learning—0
CTSyn: A Foundational Model for Cross Tabular Data Generation—0
CT-SGAN: Computed Tomography Synthesis GAN—0
CtrTab: Tabular Data Synthesis with High-Dimensional and Limited Data—0
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