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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

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Showing 631–640 of 9051 papers

TitleStatusHype
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
Diverse Cotraining Makes Strong Semi-Supervised SegmentorCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Diverse Image Captioning with Context-Object Split Latent SpacesCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
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