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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 7180 of 9051 papers

TitleStatusHype
MiniViT: Compressing Vision Transformers with Weight MultiplexingCode3
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
Addressing the Abstraction and Reasoning Corpus via Procedural Example GenerationCode3
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse LandscapesCode3
Anything-3D: Towards Single-view Anything Reconstruction in the WildCode3
Elucidating the Design Space of Multimodal Protein Language ModelsCode3
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture ModelingCode3
Generating Long Sequences with Sparse TransformersCode3
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha FactorsCode3
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