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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 771780 of 9051 papers

TitleStatusHype
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
Few-Shot Video Object DetectionCode1
FFR V1.0: Fon-French Neural Machine TranslationCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
Fine-Grained VR Sketching: Dataset and InsightsCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
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