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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 721730 of 9051 papers

TitleStatusHype
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
FHDe²Net: Full High Definition Demoireing NetworkCode1
Domain-Smoothing Network for Zero-Shot Sketch-Based Image RetrievalCode1
CodeInstruct: Empowering Language Models to Edit CodeCode1
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental SegmentationCode1
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation ApproachCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
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