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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 3451–3460 of 9051 papers

TitleStatusHype
Learning Personalized Alignment for Evaluating Open-ended Text Generation—0
Can pre-trained models assist in dataset distillation?Code1
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain—0
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization—0
MUNCH: Modelling Unique 'N Controllable Heads—0
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning—0
Multimodal Question Answering for Unified Information ExtractionCode1
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