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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 47414750 of 9051 papers

TitleStatusHype
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPUCode0
Training Deep Learning Algorithms on Synthetic Forest Images for Tree DetectionCode1
STaSy: Score-based Tabular data SynthesisCode1
Winner Takes It All: Training Performant RL Populations for Combinatorial OptimizationCode1
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
Automatic Chain of Thought Prompting in Large Language ModelsCode6
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation0
Generative Augmented Flow Networks0
Dynamic Latent Separation for Deep Learning0
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