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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 461470 of 9051 papers

TitleStatusHype
Learning Semantic Latent Directions for Accurate and Controllable Human Motion PredictionCode1
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion ModelsCode1
Visual Prompt Selection for In-Context Learning SegmentationCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph DiffusionCode1
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
Remastering Divide and Remaster: A Cinematic Audio Source Separation Dataset with Multilingual SupportCode1
Virtual Personas for Language Models via an Anthology of BackstoriesCode1
General and Task-Oriented Video SegmentationCode1
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
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