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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 4601–4625 of 9051 papers

TitleStatusHype
Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model—0
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules—0
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents—0
Attesting Distributional Properties of Training Data for Machine LearningCode0
Liquid Crystal-Based RIS for VLC Transmitters: Performance Analysis, Challenges, and Opportunities—0
Diversifying AI: Towards Creative Chess with AlphaZero—0
Text-Only Training for Visual Storytelling—0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image SegmentationCode0
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data—0
Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model—0
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping—0
Non-monotone Sequential Submodular Maximization—0
Lightweight Adaptation of Neural Language Models via Subspace EmbeddingCode0
Ranking-aware Uncertainty for Text-guided Image Retrieval—0
Emotion Embeddings x2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets—0
Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation—0
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation—0
Neural Categorical Priors for Physics-Based Character Control—0
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection—0
Controlling Character Motions without Observable Driving Source—0
Composable Core-sets for Diversity Approximation on Multi-Dataset Streams—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—0
Analyzing and controlling diversity in quantum-behaved particle swarm optimization—0
Cross-view Semantic Alignment for Livestreaming Product RecognitionCode0
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