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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 801–825 of 9051 papers

TitleStatusHype
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
Generative Category-Level Shape and Pose Estimation with Semantic PrimitivesCode1
Generative Fractional Diffusion ModelsCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
Generative Range Imaging for Learning Scene Priors of 3D LiDAR DataCode1
Are Large Language Models Capable of Generating Human-Level Narratives?Code1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised LearningCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector SpacesCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
Goals as Reward-Producing ProgramsCode1
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
Contrastive Syn-to-Real GeneralizationCode1
Graph Meta Network for Multi-Behavior RecommendationCode1
Graph Neural PDE Solvers with Conservation and Similarity-EquivarianceCode1
Controllable Multi-Interest Framework for RecommendationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
AI-generated text boundary detection with RoFTCode1
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