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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 211220 of 9051 papers

TitleStatusHype
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language ModelsCode2
MAT: Mask-Aware Transformer for Large Hole Image InpaintingCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
BirdNET: A deep learning solution for avian diversity monitoringCode2
BITS: Bi-level Imitation for Traffic SimulationCode2
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
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