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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
Anything-3D: Towards Single-view Anything Reconstruction in the WildCode3
Elucidating the Design Space of Multimodal Protein Language ModelsCode3
Addressing the Abstraction and Reasoning Corpus via Procedural Example GenerationCode3
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture ModelingCode3
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
Results of the Big ANN: NeurIPS'23 competitionCode3
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-TrainingCode2
Ambiguous Medical Image Segmentation using Diffusion ModelsCode2
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
Diverse Preference OptimizationCode2
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