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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 4151–4175 of 9051 papers

TitleStatusHype
A Robust Contrastive Alignment Method For Multi-Domain Text Classification—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
Evaluating the diversity and utility of materials proposed by generative models—0
Evaluating the Diversity and Quality of LLM Generated Content—0
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship—0
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity—0
A genomic map of the effects of linked selection in Drosophila—0
A BCS-GDE Algorithm for Multi-objective Optimization of Combined Cooling, Heating and Power Model—0
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank—0
MOSAIC: Multimodal Multistakeholder-aware Visual Art Recommendation—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Evaluating text coherence based on the graph of the consistency of phrases to identify symptoms of schizophrenia—0
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric—0
Evaluating Stochastic Rankings with Expected Exposure—0
Evaluating race and sex diversity in the world's largest companies using deep neural networks—0
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models—0
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing—0
Comment Section Personalization: Algorithmic, Interface, and Interaction Design—0
Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages—0
Is there sufficient evidence for criticality in cortical systems?—0
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?—0
A Genetic Algorithm approach to Asymmetrical Blotto Games with Heterogeneous Valuations—0
Evaluating Inclusivity, Equity, and Accessibility of NLP Technology: A Case Study for Indian Languages—0
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models—0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—0
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