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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 5676–5700 of 9051 papers

TitleStatusHype
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder—0
A Spiking Neuron Synaptic Plasticity Model Optimized for Unsupervised Learning—0
Global and local epidemiology of Group A Streptococcus indicates that naturally-acquired immunity is enduring and strain-specific—0
AlphaGarden: Learning to Autonomously Tend a Polyculture GardenCode1
Palette: Image-to-Image Diffusion ModelsCode2
Complementary Ensemble Learning—0
Towards Active Vision for Action Localization with Reactive Control and Predictive LearningCode1
Mito-nuclear selection induces a trade-off between species ecological dominance and evolutionary lifespanCode0
There is no Double-Descent in Random ForestsCode0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Increasing Data Diversity with Iterative Sampling to Improve Performance—0
Functional connectivity ensemble method to enhance BCI performance (FUCONE)Code1
Generating Diverse Realistic Laughter for Interactive Art—0
Qimera: Data-free Quantization with Synthetic Boundary Supporting SamplesCode1
Testing macroecological theories in cryptocurrency market: neutral models can not describe diversity patterns and their variation—0
Oblique and rotation double random forest—0
First experimental evaluation of ambient backscatter communications with massive MIMO reader—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
ISP-Agnostic Image Reconstruction for Under-Display Cameras—0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants—0
Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy—0
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context—0
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation NetworksCode0
CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation—0
Minimizing Annotation Effort via Max-Volume Spectral Sampling—0
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