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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 326–350 of 9051 papers

TitleStatusHype
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery DetectionCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
Dataset GrowthCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
Automating Rigid Origami DesignCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
AutoMix: Automatically Mixing Language ModelsCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Dataset Factorization for CondensationCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Active Teacher for Semi-Supervised Object DetectionCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Dan: Deep attention neural network for news recommendationCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
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