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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 21912200 of 9051 papers

TitleStatusHype
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
A comprehensive representation of selection at loci with multiple alleles that allows complex forms of genotypic fitnessCode0
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate MetricCode0
Gradient Estimators for Implicit ModelsCode0
Go Back in Time: Generating Flashbacks in Stories with Event Temporal PromptsCode0
Adversarial Inference for Multi-Sentence Video DescriptionCode0
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the WildCode0
CausalDialogue: Modeling Utterance-level Causality in ConversationsCode0
GPoeT-2: A GPT-2 Based Poem GeneratorCode0
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