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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 4276–4300 of 9051 papers

TitleStatusHype
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Co-Learning Empirical Games and World Models—0
Ensembling Sparse Autoencoders—0
Co-Learning Bayesian Optimization—0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments—0
Ensemble Squared: A Meta AutoML System—0
Ensemble Soft-Margin Softmax Loss for Image Classification—0
Ensembles of Spiking Neural Networks—0
A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"—0
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings—0
Ensembles of Random SHAPs—0
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence—0
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting—0
Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues—0
Ensembles of GANs for synthetic training data generation—0
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention—0
Ensembles of feedforward-designed convolutional neural networks—0
Oblique and rotation double random forest—0
Coherent FDA Radar: Transmitter and Receiver Design and Analysis—0
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation—0
Ensemble Pruning via Margin Maximization—0
Ensemble pruning via an integer programming approach with diversity constraints—0
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