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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 201210 of 9051 papers

TitleStatusHype
Training-Free Consistent Text-to-Image GenerationCode2
Retrieval-Augmented Score Distillation for Text-to-3D GenerationCode2
SuperCLUE-Math6: Graded Multi-Step Math Reasoning Benchmark for LLMs in ChineseCode2
Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic RoomsCode2
Integrate Any Omics: Towards genome-wide data integration for patient stratificationCode2
Large Language Models Can Learn Temporal ReasoningCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
SVGDreamer: Text Guided SVG Generation with Diffusion ModelCode2
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAXCode2
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