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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 3226–3250 of 9051 papers

TitleStatusHype
Partially Randomizing Transformer Weights for Dialogue Response Diversity—0
Diverse Shape Completion via Style Modulated Generative Adversarial Networks—0
Unsupervised Estimation of Ensemble Accuracy—0
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images—0
An Empirical Bayes Framework for Open-Domain Dialogue Generation—0
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression—0
Optimal navigability of weighted human brain connectomes in physical space—0
Hierarchical Pruning of Deep Ensembles with Focal DiversityCode0
JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language ProcessingCode0
The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic TextCode0
Program-Aided Reasoners (better) Know What They KnowCode0
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations—0
GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language UnderstandingCode0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
Attribute Diversity Determines the Systematicity Gap in VQACode0
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Safer-Instruct: Aligning Language Models with Automated Preference DataCode1
MC^2: Towards Transparent and Culturally-Aware NLP for Minority Languages in ChinaCode1
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations—0
AI-generated text boundary detection with RoFTCode1
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications—0
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation—0
Towards Reasoning in Large Language Models via Multi-Agent Peer Review CollaborationCode1
Self-Evolved Diverse Data Sampling for Efficient Instruction TuningCode1
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion—0
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