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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 37213730 of 9051 papers

TitleStatusHype
Multilingual Lexical Simplification via Paraphrase GenerationCode0
Exploring Format Consistency for Instruction TuningCode0
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?Code0
Reconciling the accuracy-diversity trade-off in recommendations0
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point TrackingCode2
EqGAN: Feature Equalization Fusion for Few-shot Image Generation0
Self-Supervised Visual Acoustic Matching0
Data Augmentation for Neural Machine Translation using Generative Language Model0
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
Retinotopy Inspired Brain Encoding Model and the All-for-One Training Recipe0
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