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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 2501–2525 of 9051 papers

TitleStatusHype
Distinguishing Cell Phenotype Using Cell EpigenotypeCode0
GAIT: A Geometric Approach to Information TheoryCode0
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-TrainingCode0
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and GeneralizationCode0
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-NetworkCode0
Better RAG using Relevant Information GainCode0
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and DiversityCode0
Distributional Discrepancy: A Metric for Unconditional Text GenerationCode0
Game Theory for Adversarial Attacks and DefensesCode0
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and DiversityCode0
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised LearningCode0
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing FlowCode0
Differentially Private Learning Needs Better Model Initialization and Self-DistillationCode0
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility MetricCode0
How to partition diversityCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
Full-Stack Filters to Build Minimum Viable CNNsCode0
An Automated Ensemble Learning Framework Using Genetic Programming for Image ClassificationCode0
From structure mining to unsupervised exploration of atomic octahedral networksCode0
From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMsCode0
Fully Automatic Video Colorization with Self-Regularization and DiversityCode0
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual MatchingCode0
Differentiable Instruction Optimization for Cross-Task GeneralizationCode0
From Local to Global: Navigating Linguistic Diversity in the African ContextCode0
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