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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 17211730 of 9051 papers

TitleStatusHype
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
Information Density Principle for MLLM BenchmarksCode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
Investigating Metric Diversity for Evaluating Long Document SummarisationCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
IndicEval-XL: Bridging Linguistic Diversity in Code Generation Across Indic LanguagesCode0
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