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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 13511360 of 9051 papers

TitleStatusHype
Building a Conversational Agent Overnight with Dialogue Self-PlayCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
Keyphrase Generation with Cross-Document AttentionCode1
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D inputCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language ModelsCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
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