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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 27812790 of 9051 papers

TitleStatusHype
FairER: Entity Resolution with Fairness ConstraintsCode0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Another Diversity-Promoting Objective Function for Neural Dialogue GenerationCode0
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIsCode0
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness RewardCode0
Analysing domain shift factors between videos and images for object detectionCode0
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic EmbeddingsCode0
FaceCoresetNet: Differentiable Coresets for Face Set RecognitionCode0
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