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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 281290 of 9051 papers

TitleStatusHype
Freeform Body Motion Generation from SpeechCode2
A Contrastive Framework for Neural Text GenerationCode2
InPars: Data Augmentation for Information Retrieval using Large Language ModelsCode2
Accelerated Quality-Diversity through Massive ParallelismCode2
Trajectory balance: Improved credit assignment in GFlowNetsCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
ShapeFormer: Transformer-based Shape Completion via Sparse RepresentationCode2
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty LearningCode2
Palette: Image-to-Image Diffusion ModelsCode2
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