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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 41–50 of 9051 papers

TitleStatusHype
AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario—0
State-Space Models in Efficient Whispered and Multi-dialect Speech Recognition—0
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Structured and Informed Probabilistic Modeling with the Thermodynamic Kolmogorov-Arnold ModelCode0
M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models—0
orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels—0
AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMsCode0
Mitigating Safety Fallback in Editing-based Backdoor Injection on LLMsCode0
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