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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 961970 of 9051 papers

TitleStatusHype
STaSy: Score-based Tabular data SynthesisCode1
Training Deep Learning Algorithms on Synthetic Forest Images for Tree DetectionCode1
Winner Takes It All: Training Performant RL Populations for Combinatorial OptimizationCode1
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI SolutionsCode1
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill DiscoveryCode1
AlphaFold Distillation for Protein DesignCode1
Reprogramming Pretrained Language Models for Antibody Sequence InfillingCode1
The Vendi Score: A Diversity Evaluation Metric for Machine LearningCode1
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
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