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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 701710 of 9051 papers

TitleStatusHype
Diversity-based Trajectory and Goal Selection with Hindsight Experience ReplayCode1
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arraysCode1
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping DatasetsCode1
Domain-Smoothing Network for Zero-Shot Sketch-Based Image RetrievalCode1
Domain-Unified Prompt Representations for Source-Free Domain GeneralizationCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
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