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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 3201–3225 of 9051 papers

TitleStatusHype
Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach—0
Metaheuristic Enhanced with Feature-Based Guidance and Diversity Management for Solving the Capacitated Vehicle Routing Problem—0
Toward Wireless Localization Using Multiple Reconfigurable Intelligent Surfaces—0
CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in HealthcareCode0
Evolution of cooperation with Q-learning: the impact of information perception—0
Specify and Edit: Overcoming Ambiguity in Text-Based Image EditingCode0
Towards Open-Set Myoelectric Gesture Recognition via Dual-Perspective Inconsistency Learning—0
Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle—0
Genetic Algorithm-based Routing and Scheduling for Wildfire Suppression using a Team of UAVs—0
Constructing Enhanced Mutual Information for Online Class-Incremental Learning—0
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model—0
The BIAS Detection Framework: Bias Detection in Word Embeddings and Language Models for European LanguagesCode0
GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and DovesCode0
Multipath Identification and Mitigation with FDA-MIMO Radar—0
Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey—0
The FIGNEWS Shared Task on News Media Narratives—0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Network Inversion of Convolutional Neural Nets—0
Enhancing Diversity in Multi-objective Feature Selection—0
PEFT-U: Parameter-Efficient Fine-Tuning for User PersonalizationCode0
Image Segmentation via Divisive Normalization: dealing with environmental diversity—0
XS-VID: An Extremely Small Video Object Detection Dataset—0
Ontology of Belief Diversity: A Community-Based Epistemological Approach—0
Pose Estimation from Camera Images for Underwater Inspection—0
Diversity in Choice as Majorization—0
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