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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 5976–6000 of 9051 papers

TitleStatusHype
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective—0
Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics—0
Understanding attention-based encoder-decoder networks: a case study with chess scoresheet recognition—0
Understanding Deformable Alignment in Video Super-Resolution—0
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup—0
Attention Mechanism for LLM-based Agents Dynamic Diffusion under Information Asymmetry—0
Understanding Everyday Hands in Action From RGB-D Images—0
Understanding fitness landscapes in morpho-evolution via local optima networks—0
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing—0
Understanding Likelihood Over-optimisation in Direct Alignment Algorithms—0
Understanding Model Ensemble in Transferable Adversarial Attack—0
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection—0
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle—0
Understanding the Limitations of Diffusion Concept Algebra Through Food—0
Understanding the Role of Functional Diversity in Weight-Ensembling with Ingredient Selection and Multidimensional Scaling—0
Understanding the Role of Temperature in Diverse Question Generation by GPT-4—0
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning—0
Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management—0
Understanding Unnatural Questions Improves Reasoning over Text—0
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks—0
UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning—0
UniAvatar: Taming Lifelike Audio-Driven Talking Head Generation with Comprehensive Motion and Lighting Control—0
UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models—0
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation—0
Unification of Balti and trans-border sister dialects in the essence of LLMs and AI Technology—0
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