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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 52715280 of 9051 papers

TitleStatusHype
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages0
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling0
SigVIC: Spatial Importance Guided Variable-Rate Image Compression0
SiLK: Simple Learned Keypoints0
SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation0
Sim2real transfer learning for 3D human pose estimation: motion to the rescue0
SimGen: Simulator-conditioned Driving Scene Generation0
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition0
Similarity-based Learning via Data Driven Embeddings0
Similarity-Based Reconstruction Loss for Meaning Representation0
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