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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 5276–5300 of 9051 papers

TitleStatusHype
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks—0
LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and High-pass Views—0
Automatic time-series phenotyping using massive feature extraction—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Acquisition of Recursive Possessives and Recursive Locatives in Mandarin—0
LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions—0
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning—0
Long-tailed Recognition by Learning from Latent Categories—0
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization—0
Long-Tail Predictions with Continuous-Output Language Models—0
Long-tail Session-based Recommendation—0
A Coverage-Guided Testing Framework for Quantum Neural Networks—0
Long-term sustained malaria control leads to inbreeding and fragmentation of Plasmodium vivax populations—0
Long Video Diffusion Generation with Segmented Cross-Attention and Content-Rich Video Data Curation—0
Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning—0
LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images—0
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity—0
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences—0
A Convergence indicator for Multi-Objective Optimisation Algorithms—0
Lookism: The overlooked bias in computer vision—0
"Look Ma, No Hands!" A Parameter-Free Topic Model—0
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving—0
Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations—0
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation—0
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function—0
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