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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
Generating Long Sequences with Sparse TransformersCode3
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics AwarenessCode3
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
Improving Text Embeddings with Large Language ModelsCode3
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
Elucidating the Design Space of Multimodal Protein Language ModelsCode3
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture ModelingCode3
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha FactorsCode3
Addressing the Abstraction and Reasoning Corpus via Procedural Example GenerationCode3
Anything-3D: Towards Single-view Anything Reconstruction in the WildCode3
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