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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 1281–1290 of 9051 papers

TitleStatusHype
Topic-Conversation Relevance (TCR) Dataset and BenchmarksCode1
FairSkin: Fair Diffusion for Skin Disease Image Generation—0
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks—0
BENCHAGENTS: Automated Benchmark Creation with Agent Interaction—0
GRADE: Quantifying Sample Diversity in Text-to-Image Models—0
ReMix: Training Generalized Person Re-identification on a Mixture of Data—0
On the Statistical Complexity of Estimating VENDI Scores from Empirical Data—0
On the Role of Depth and Looping for In-Context Learning with Task Diversity—0
Can Users Detect Biases or Factual Errors in Generated Responses in Conversational Information-Seeking?Code0
Reducing the Scope of Language Models—0
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