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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 6170 of 9051 papers

TitleStatusHype
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics AwarenessCode3
Improving Text Embeddings with Large Language ModelsCode3
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
Generating Long Sequences with Sparse TransformersCode3
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse LandscapesCode3
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture ModelingCode3
Anything-3D: Towards Single-view Anything Reconstruction in the WildCode3
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