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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

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Showing 5111–5120 of 9051 papers

TitleStatusHype
Sampling Approach Matters: Active Learning for Robotic Language Acquisition—0
Sampling from Probabilistic Submodular Models—0
Contextually Plausible and Diverse 3D Human Motion Prediction—0
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English—0
Sampling Strategies for GAN Synthetic Data—0
Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets—0
Efficient and Training-Free Control of Language Generation—0
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.—0
SANE: Specialization-Aware Neural Network Ensemble—0
SAOR: Single-View Articulated Object Reconstruction—0
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