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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 3351–3375 of 9051 papers

TitleStatusHype
MCC-KD: Multi-CoT Consistent Knowledge DistillationCode0
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Diverse Priors for Deep Reinforcement Learning—0
Invariant Feature Regularization for Fair Face RecognitionCode1
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text GenerationCode0
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Iteratively Learn Diverse Strategies with State Distance Information—0
Neural Multi-Objective Combinatorial Optimization with Diversity EnhancementCode1
Semantic and Expressive Variation in Image Captions Across Languages—0
Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems—0
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation—0
Diversity Preferences, Affirmative Action and Choice Rules—0
Shortcuts for causal discovery of nonlinear models by score matching—0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Knowledge Graph Context-Enhanced Diversified RecommendationCode0
Controlled Randomness Improves the Performance of Transformer Models—0
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models—0
PSGText: Stroke-Guided Scene Text Editing with PSP Module—0
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
Fundamental Limits of Membership Inference Attacks on Machine Learning Models—0
Time-Modulated Intelligent Reflecting Surface for Waveform Security—0
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
Benchmarking and Improving Text-to-SQL Generation under AmbiguityCode0
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