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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 271280 of 9051 papers

TitleStatusHype
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about ChangeCode2
SCAMPS: Synthetics for Camera Measurement of Physiological SignalsCode2
Text2Human: Text-Driven Controllable Human Image GenerationCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single ImageCode2
MAT: Mask-Aware Transformer for Large Hole Image InpaintingCode2
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question AnsweringCode2
Stochastic Trajectory Prediction via Motion Indeterminacy DiffusionCode2
Deep Rectangling for Image Stitching: A Learning BaselineCode2
L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained EnvironmentsCode2
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