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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 1101–1125 of 9051 papers

TitleStatusHype
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
Adaptively Sparse TransformersCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Barbie: Text to Barbie-Style 3D AvatarsCode1
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
Deep Image Harmonization with Learnable AugmentationCode1
Deep Ordinal Regression with Label DiversityCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Multi-Interest Framework for RecommendationCode1
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
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