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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 8190 of 9051 papers

TitleStatusHype
RT-1: Robotics Transformer for Real-World Control at ScaleCode3
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative ModelsCode3
MiniViT: Compressing Vision Transformers with Weight MultiplexingCode3
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
MNN: A Universal and Efficient Inference EngineCode3
Generating Long Sequences with Sparse TransformersCode3
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics EmulationCode2
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based AgentsCode2
Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMsCode2
UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian SplattingCode2
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