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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 3651–3675 of 9051 papers

TitleStatusHype
Diversidade linguística e inclusão digital: desafios para uma ia brasileira—0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect—0
An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP—0
Diverse Yet Efficient Retrieval using Hash Functions—0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation—0
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands—0
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings—0
Feedback-based Approach to Introduce Freshness in Recommendations—0
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out—0
Generalized Lotka-Volterra systems with quenched random interactions and saturating functional response—0
iTool: Boosting Tool Use of Large Language Models via Iterative Reinforced Fine-Tuning—0
Diverse Video Captioning Through Latent Variable Expansion—0
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size—0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms—0
Few-shot 3D Shape Generation—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court—0
Diverse Trajectory Forecasting with Determinantal Point Processes—0
Few-shot Image Generation Using Discrete Content Representation—0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
Diverse Single Image Generation with Controllable Global Structure—0
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