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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 2931–2940 of 9051 papers

TitleStatusHype
BRUDEX Database: Binaural Room Impulse Responses with Uniformly Distributed External Microphones—0
AnyFace: Free-style Text-to-Face Synthesis and Manipulation—0
DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization—0
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning—0
Diversity-Aware Reinforcement Learning for de novo Drug Design—0
DUM: Diversity-Weighted Utility Maximization for Recommendations—0
CG-NeRF: Conditional Generative Neural Radiance Fields—0
DVCFlow: Modeling Information Flow Towards Human-like Video Captioning—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Diversity-Aware Policy Optimization for Large Language Model Reasoning—0
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