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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 1801–1825 of 9051 papers

TitleStatusHype
Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
Context-Enhanced Detector For Building Detection From Remote Sensing Images—0
AID++: An Updated Version of AID on Scene Classification—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
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
Continual Semantic Segmentation with Automatic Memory Sample Selection—0
Continual Speaker Adaptation for Text-to-Speech Synthesis—0
Constraint-based Diversification of JOP Gadgets—0
Continuous Inference in Graphical Models with Polynomial Energies—0
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization—0
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality—0
Contrastive Examples for Addressing the Tyranny of the Majority—0
Assessment of Practical Smart Gateway Diversity Based on Multi-Site Measurements in Q/V band—0
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
Contrastive Learning for Diverse Disentangled Foreground Generation—0
Contrastive Learning from Synthetic Audio Doppelgängers—0
Embracing Diversity: A Multi-Perspective Approach with Soft Labels—0
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