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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 8501–8525 of 9051 papers

TitleStatusHype
Finding A Voice: Evaluating African American Dialect Generation for Chatbot TechnologyCode0
Mostly Exploration-Free Algorithms for Contextual BanditsCode0
ReactDiff: Latent Diffusion for Facial Reaction GenerationCode0
Motif-Centric Representation Learning for Symbolic MusicCode0
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text GenerationCode0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
Unsupervised Boosting-based Autoencoder Ensembles for Outlier DetectionCode0
WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound DatabaseCode0
REAL: A Representative Error-Driven Approach for Active LearningCode0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out CodesCode0
MovieQA: Understanding Stories in Movies through Question-AnsweringCode0
Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imagingCode0
A Patch-Based Algorithm for Diverse and High Fidelity Single Image GenerationCode0
Divergent Ensemble Networks: Enhancing Uncertainty Estimation with Shared Representations and Independent BranchingCode0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate MetricCode0
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image RetrievalCode0
Theoretical Aspects of Bias and Diversity in Minimum Bayes Risk DecodingCode0
MST5 -- Multilingual Question Answering over Knowledge GraphsCode0
MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor SegmentationCode0
SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different LanguagesCode0
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier IntegralsCode0
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGCode0
Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language ModelCode0
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