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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 15311540 of 9051 papers

TitleStatusHype
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Process-Supervised LLM Recommenders via Flow-guided TuningCode1
Profiling Pareto Front With Multi-Objective Stein Variational Gradient DescentCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
A Map of Diverse Synthetic Stable Roommates InstancesCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Conditioned Query Generation for Task-Oriented Dialogue SystemsCode1
Class-Balancing Diffusion ModelsCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
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