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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 2401–2410 of 9051 papers

TitleStatusHype
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in HanabiCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language ModelsCode0
3D2M Dataset: A 3-Dimension diverse Mesh DatasetCode0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear BanditsCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT ImagesCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
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