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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 10811090 of 9051 papers

TitleStatusHype
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide GenerationCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Multi-Interest Framework for RecommendationCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
Bacteriophage classification for assembled contigs using Graph Convolutional NetworkCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
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