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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 291300 of 9051 papers

TitleStatusHype
BirdNET: A deep learning solution for avian diversity monitoringCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
Bridging Remote Sensors with Multisensor Geospatial Foundation ModelsCode2
FLatten Transformer: Vision Transformer using Focused Linear AttentionCode2
Deep Rectangling for Image Stitching: A Learning BaselineCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
BITS: Bi-level Imitation for Traffic SimulationCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAXCode2
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