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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 6251–6275 of 9051 papers

TitleStatusHype
Operational vs Convolutional Neural Networks for Image Denoising—0
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study—0
Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content—0
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research—0
Opposition Based ElectromagnetismLike for Global Optimization—0
Opposition based Ensemble Micro Differential Evolution—0
Understanding Deformable Alignment in Video Super-Resolution—0
Optical Field Recovery in Jones Space—0
Optical Wireless cabin communication system—0
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup—0
Optimal Budgeted Rejection Sampling for Generative Models—0
Optimal compound downselection to promote diversity and parallel chemistry—0
A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming—0
Optimal Execution Using Reinforcement Learning—0
A Review on Quantile Regression for Stochastic Computer Experiments—0
AI for All: Identifying AI incidents Related to Diversity and Inclusion—0
Optimal navigability of weighted human brain connectomes in physical space—0
Optimal Selective Attention in Reactive Agents—0
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain—0
Optimal Transport Based Generative Autoencoders—0
Optimisation of federated learning settings under statistical heterogeneity variations—0
Attention Mechanism for LLM-based Agents Dynamic Diffusion under Information Asymmetry—0
Understanding Everyday Hands in Action From RGB-D Images—0
A review of TinyML—0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
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