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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 3451–3475 of 9051 papers

TitleStatusHype
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces—0
Active Learning on Synthons for Molecular Design—0
Context-Enhanced Detector For Building Detection From Remote Sensing Images—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
Artificial Intelligence Development Races in Heterogeneous Settings—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)—0
A Supervised Segmentation Network for Hyperspectral Image Classification—0
Content-Diverse Comparisons improve IQA—0
A study of quality and diversity in K+1 GANs—0
AIDE: Task-Specific Fine Tuning with Attribute Guided Multi-Hop Data Expansion—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Construction of Responsive Utterance Corpus for Attentive Listening Response Production—0
A study of conceptual language similarity: comparison and evaluation—0
Construction and optimization of health behavior prediction model for the elderly in smart elderly care—0
Constructing Enhanced Mutual Information for Online Class-Incremental Learning—0
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations—0
Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge—0
Constructing a meta-learner for unsupervised anomaly detection—0
A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data—0
Constraint-Based Software Diversification for Efficient Mitigation of Code-Reuse Attacks—0
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