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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 17311740 of 9051 papers

TitleStatusHype
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
A Block-Based Adaptive Decoupling Framework for Graph Neural NetworksCode0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
Increasing Entropy to Boost Policy Gradient Performance on Personalization TasksCode0
A Hybrid Retrieval-Generation Neural Conversation ModelCode0
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical StudyCode0
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with DroneCode0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
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