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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
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and TrackingCode2
Conditional GANs with Auxiliary Discriminative ClassifierCode2
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding TasksCode2
Diffusion Models Beat GANs on Image SynthesisCode2
BirdNET: A deep learning solution for avian diversity monitoringCode2
The Pile: An 800GB Dataset of Diverse Text for Language ModelingCode2
Improved StyleGAN Embedding: Where are the Good Latents?Code2
Multi-Objective Molecule Generation using Interpretable SubstructuresCode2
Scalability in Perception for Autonomous Driving: Waymo Open DatasetCode2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
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