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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

Showing 751775 of 9051 papers

TitleStatusHype
Apples to Apples: A Systematic Evaluation of Topic ModelsCode1
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer ModelsCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Controllable Multi-Interest Framework for RecommendationCode1
Exploiting Abstract Meaning Representation for Open-Domain Question AnsweringCode1
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Controllable Video Captioning with an Exemplar SentenceCode1
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic FacesCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain GeneralizationCode1
Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring TechniqueCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
Few-Shot Video Object DetectionCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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