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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 6626–6650 of 9051 papers

TitleStatusHype
Locomotion-Action-Manipulation: Synthesizing Human-Scene Interactions in Complex 3D Environments—0
LOGen: Toward Lidar Object Generation by Point Diffusion—0
Logistic Regression Analysis on the Dietary Behavior and the Risk of Nutritional Deficiency Dermatosis: The Case of Bicol Region, Philippines—0
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks—0
LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and High-pass Views—0
LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions—0
Long-tailed Recognition by Learning from Latent Categories—0
Long-Tail Predictions with Continuous-Output Language Models—0
Long-tail Session-based Recommendation—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
LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images—0
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences—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
Loss-based Sequential Learning for Active Domain Adaptation—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNets—0
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation—0
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation—0
Low-Complexity Linear Diversity-Combining Detector for MIMO-OTFS—0
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