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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 18711880 of 9051 papers

TitleStatusHype
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial OptimizationCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments0
Can time series forecasting be automated? A benchmark and analysis0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift0
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations0
Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph GenerationCode1
Visual-Semantic Decomposition and Partial Alignment for Document-based Zero-Shot LearningCode0
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealingCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
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