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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 4351–4375 of 9051 papers

TitleStatusHype
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Sample-efficient Multi-objective Molecular Optimization with GFlowNetsCode1
Why the Mansfield Rule can't work: a supply demand analysis—0
Cluster Index Modulation for Reconfigurable Intelligent Surface-Assisted mmWave Massive MIMO—0
Mask Conditional Synthetic Satellite ImageryCode1
Performative Recommendation: Diversifying Content via Strategic IncentivesCode0
MMPD: Multi-Domain Mobile Video Physiology DatasetCode1
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous SpeechCode2
Effective Data Augmentation With Diffusion ModelsCode2
Ethical Considerations for Responsible Data CurationCode0
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
Diverse Probabilistic Trajectory Forecasting with Admissibility ConstraintsCode0
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication—0
DivBO: Diversity-aware CASH for Ensemble Learning—0
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—0
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form ControlCode1
Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy—0
This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation—0
Diversity Induced Environment Design via Self-Play—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization—0
Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors—0
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization—0
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