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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 6701–6725 of 9051 papers

TitleStatusHype
Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition—0
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation—0
Mapping ChatGPT in Mainstream Media to Unravel Jobs and Diversity Challenges: Early Quantitative Insights through Sentiment Analysis and Word Frequency Analysis—0
MapQA: Open-domain Geospatial Question Answering on Map Data—0
MaRI: Material Retrieval Integration across Domains—0
Mark-Evaluate: Assessing Language Generation using Population Estimation Methods—0
Markov Decision Process for Video Generation—0
MarsCode Agent: AI-native Automated Bug Fixing—0
Mask-Guided Matting in the Wild—0
Mask-Guided Portrait Editing with Conditional GANs—0
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction—0
MATCHA: Can Multi-Agent Collaboration Build a Trustworthy Conversational Recommender?—0
Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization—0
Material Classification With Thermal Imagery—0
Mathematical Modeling Analysis and Optimization of Fungal Diversity Growth—0
Mathematical toy model inspired by the problem of the adaptive origins of the sexual orientation continuum—0
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model—0
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints—0
Matina: A Large-Scale 73B Token Persian Text Corpus—0
Matrix Factorization Equals Efficient Co-occurrence Representation—0
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling—0
Max and Coincidence Neurons in Neural Networks—0
Max-Diversity Distributed Learning: Theory and Algorithms—0
Maximally Separated Active Learning—0
Maximizing Diversity for Multimodal Optimization—0
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