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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

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Showing 701–725 of 9051 papers

TitleStatusHype
The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data—0
Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary EvaluationCode0
GenIAS: Generator for Instantiating Anomalies in time Series—0
Measuring Diversity in Synthetic DatasetsCode1
Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms—0
Scaling Pre-training to One Hundred Billion Data for Vision Language Models—0
We Can't Understand AI Using our Existing Vocabulary—0
Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model EvaluationCode0
Unsupervised Translation of Emergent Communication—0
On properties driving diversity index selection—0
RenderBox: Expressive Performance Rendering with Text Control—0
exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment ProblemCode0
Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains—0
Many-Task Federated Fine-Tuning via Unified Task Vectors—0
Select before Act: Spatially Decoupled Action Repetition for Continuous Control—0
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoECode1
TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints—0
Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMs—0
Universal point spread function engineering for 3D optical information processing—0
Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related QueriesCode0
Pencils to Pixels: A Systematic Study of Creative Drawings across Children, Adults and AICode0
Knowledge Graph-Guided Retrieval Augmented GenerationCode2
Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive Fusion—0
A Tutorial On Intersectionality in Fair Rankings—0
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety—0
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