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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 34013410 of 9051 papers

TitleStatusHype
Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration0
Incentive Mechanism Design for Distributed Ensemble Learning0
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
Dialect Transfer for Swiss German Speech Translation0
Analysing of 3D MIMO Communication Beamforming in Linear and Planar Arrays0
Towards Evaluating Generalist Agents: An Automated Benchmark in Open WorldCode1
Evolutionary Dynamic Optimization and Machine Learning0
Kernel-Elastic Autoencoder for Molecular Design0
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous DrivingCode3
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