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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 15011510 of 9051 papers

TitleStatusHype
APOLLO: An Optimized Training Approach for Long-form Numerical ReasoningCode1
S2FGAN: Semantically Aware Interactive Sketch-to-Face TranslationCode1
Apples to Apples: A Systematic Evaluation of Topic ModelsCode1
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer ModelsCode1
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
GLAMOUR: Graph Learning over Macromolecule RepresentationsCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Sample-efficient Multi-objective Molecular Optimization with GFlowNetsCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Improving Adversarial Transferability with Gradient RefiningCode1
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