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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 15411550 of 9051 papers

TitleStatusHype
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
FastGrasp: Efficient Grasp Synthesis with DiffusionCode1
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
Feature Fusion from Head to Tail for Long-Tailed Visual RecognitionCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
Sieve: Multimodal Dataset Pruning Using Image Captioning ModelsCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
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