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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 14311440 of 9051 papers

TitleStatusHype
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation ApproachCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial LearningCode1
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM ImagesCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental SegmentationCode1
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal AnglesCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Answering Ambiguous Questions via Iterative PromptingCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
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