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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 331340 of 9051 papers

TitleStatusHype
A Case for Rejection in Low Resource ML DeploymentCode1
Dan: Deep attention neural network for news recommendationCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
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