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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 2211–2220 of 9051 papers

TitleStatusHype
Layout Agnostic Scene Text Image Synthesis with Diffusion Models—0
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization FunctionCode0
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
Automatic Fused Multimodal Deep Learning for Plant IdentificationCode0
Multipath Exploitation for Fluctuating Target Detection in RIS-Assisted ISAC Systems—0
Lasso Bandit with Compatibility Condition on Optimal Arm—0
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
Image Captioning via Dynamic Path CustomizationCode0
Achieving Distributed MIMO Performance with Repeater-Assisted Cellular Massive MIMO—0
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