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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 541550 of 9051 papers

TitleStatusHype
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion ModelsCode1
Differential Evolution with Reversible Linear TransformationsCode1
Differentiable Quality DiversityCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
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