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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 711720 of 9051 papers

TitleStatusHype
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Diversity-Guided Multi-Objective Bayesian Optimization With Batch EvaluationsCode1
FastGrasp: Efficient Grasp Synthesis with DiffusionCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Answering Ambiguous Questions via Iterative PromptingCode1
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationCode1
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