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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 2376–2400 of 9051 papers

TitleStatusHype
LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and High-pass Views—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs—0
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge—0
Universal Features Guided Zero-Shot Category-Level Object Pose Estimation—0
TARDiS : Text Augmentation for Refining Diversity and Separability—0
Pixel-Wise Feature Selection for Perceptual Edge Detection without post-processing—0
Citation Structural Diversity: A Novel and Concise Metric Combining Structure and Semantics for Literature Evaluation—0
Facial Attractiveness Prediction in Live Streaming: A New Benchmark and Multi-modal Method—0
Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language UnderstandingCode0
CORD: Generalizable Cooperation via Role Diversity—0
The Application of Large Language Models in Recommendation Systems—0
ArtCrafter: Text-Image Aligning Style Transfer via Embedding Reframing—0
AR4D: Autoregressive 4D Generation from Monocular Videos—0
Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion—0
Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering—0
The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation—0
Improving Accuracy and Calibration via Differentiated Deep Mutual Learning—0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
Enhancing Diversity for Data-free Quantization—0
Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement LearningCode0
DIVE: Diversified Iterative Self-ImprovementCode0
DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
Unity in Diversity: Video Editing via Gradient-Latent Purification—0
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