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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 401410 of 9051 papers

TitleStatusHype
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal TransferCode1
Deep Ordinal Regression with Label DiversityCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
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