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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 30513060 of 9051 papers

TitleStatusHype
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
ETS: Efficient Tree Search for Inference-Time ScalingCode0
A mirror-Unet architecture for PET/CT lesion segmentationCode0
EuLearn: A 3D database for learning Euler characteristicsCode0
Data Fusion for Deep Learning on Transport Mode Detection: A Case StudyCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Counterfactual reasoning: an analysis of in-context emergenceCode0
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic ExpressionsCode0
Data-free Knowledge Distillation for Segmentation using Data-Enriching GANCode0
Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of MetricsCode0
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