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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 1426–1450 of 9051 papers

TitleStatusHype
FHDe²Net: Full High Definition Demoireing NetworkCode1
IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News ArticlesCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
Comprehensive Image Captioning via Scene Graph DecompositionCode1
End-to-End Optimization of Scene LayoutCode1
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingCode1
Regularizing Deep Networks with Semantic Data AugmentationCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
Length-Controllable Image CaptioningCode1
On Disentangling Spoof Trace for Generic Face Anti-SpoofingCode1
When and how CNNs generalize to out-of-distribution category-viewpoint combinationsCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial NetworkCode1
Sequence Generation with Mixed RepresentationsCode1
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge BaseCode1
Learning to Generate Novel Domains for Domain GeneralizationCode1
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image RecognitionCode1
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer ModelsCode1
HausaMT v1.0: Towards English--Hausa Neural Machine TranslationCode1
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue GenerationCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Deep Ordinal Regression with Label DiversityCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
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