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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 12111220 of 9051 papers

TitleStatusHype
Intra-Source Style Augmentation for Improved Domain GeneralizationCode1
Invariant Feature Regularization for Fair Face RecognitionCode1
Building a Conversational Agent Overnight with Dialogue Self-PlayCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine TranslationCode1
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
General and Task-Oriented Video SegmentationCode1
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