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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 8026–8050 of 9051 papers

TitleStatusHype
Shadow Transfer: Single Image Relighting For Urban Road Scenes—0
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning—0
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
DDS: A new device-degraded speech dataset for speech enhancement—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Decentralized Air Traffic Management for Advanced Air Mobility—0
Decentralized Collaborative Learning with Probabilistic Data Protection—0
Artificial Intelligence in Election Campaigns: Perceptions, Penalties, and Implications—0
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs—0
Cooperative UAV-Relay based Satellite Aerial Ground Integrated Networks—0
Decoding and Diversity in Machine Translation—0
Cooperative Intergroup Mating Can Overcome Ethnocentrism in Diverse Populations—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics—0
Decoding the Diversity: A Review of the Indic AI Research Landscape—0
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform—0
Decomposed evaluations of geographic disparities in text-to-image models—0
SURF: Semantic-level Unsupervised Reward Function for Machine Translation—0
Decomposition of the Leinster-Cobbold Diversity Index—0
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Decoupling Pragmatics: Discriminative Decoding for Referring Expression Generation—0
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