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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 2801–2825 of 9051 papers

TitleStatusHype
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
A Multimodal In-Context Tuning Approach for E-Commerce Product Description GenerationCode1
Geometry-Informed Neural NetworksCode2
WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound DatabaseCode0
Robust Model Predictive Control for nonlinear discrete-time systems using iterative time-varying constraint tightening—0
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickCode0
NeRF Solves Undersampled MRI Reconstruction—0
wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7TCode1
Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation—0
Parallel Structures in Pre-training Data Yield In-Context LearningCode0
MGF: Mixed Gaussian Flow for Diverse Trajectory PredictionCode1
Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid Federated Approach—0
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models—0
PreAct: Prediction Enhances Agent's Planning AbilityCode1
MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions—0
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning—0
ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual ConnectionsCode1
Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation StrategiesCode1
Python is Not Always the Best Choice: Embracing Multilingual Program of ThoughtsCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Exploring Precision and Recall to assess the quality and diversity of LLMsCode0
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data—0
Instruction Diversity Drives Generalization To Unseen Tasks—0
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language ModelsCode1
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation—0
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