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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 851860 of 9051 papers

TitleStatusHype
Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking DatasetCode1
De novo Drug Design using Reinforcement Learning with Multiple GPT AgentsCode1
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Dense Relational Captioning: Triple-Stream Networks for Relationship-Based CaptioningCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
KERPLE: Kernelized Relative Positional Embedding for Length ExtrapolationCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
Active Learning by Acquiring Contrastive ExamplesCode1
End-to-End Optimization of Scene LayoutCode1
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