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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 8476–8500 of 9051 papers

TitleStatusHype
Diverse Imagenet Models Transfer Better—0
Diverse Image Style Transfer via Invertible Cross-Space Mapping—0
Computational historical linguistics and language diversity in South Asia—0
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics—0
Diverse Large-Scale ITS Dataset Created from Continuous Learning for Real-Time Vehicle Detection—0
Diverse legal case search—0
Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Model—0
System Description for the CommonGen task with the POINTER model—0
Diverse Machine Translation with a Single Multinomial Latent Variable—0
Diverse Melody Generation from Chinese Lyrics via Mutual Information Maximization—0
Diverse mini-batch Active Learning—0
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion—0
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
Diverse Neural Network Learns True Target Functions—0
Diverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models—0
Offline Diversity Maximization Under Imitation Constraints—0
Advances in Multi-turn Dialogue Comprehension: A Survey—0
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer—0
Systems, Actors and Agents: Operation in a multicomponent environment—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Computational Eco-Systems for Handwritten Digits Recognition—0
Compression using Discrete Multi-Level Divisor Transform for Heterogeneous Sensor Data—0
Diverse Priors for Deep Reinforcement Learning—0
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