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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 2721–2730 of 9051 papers

TitleStatusHype
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
Diversity driven Query Rewriting in Search Advertising—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
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