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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 1251–1260 of 9051 papers

TitleStatusHype
Learning Object Placement via Dual-path Graph CompletionCode1
Learning Semantic-Aligned Feature Representation for Text-based Person SearchCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
Dataset GrowthCode1
Generative Data Augmentation for Aspect Sentiment Quad PredictionCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Toward a Plug-and-Play Vision-Based Grasping Module for RoboticsCode1
Biological Sequence Design with GFlowNetsCode1
Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVCode1
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