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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 5326–5350 of 9051 papers

TitleStatusHype
Automatic event detection in microblogs using incremental machine learning—0
M^3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset—0
M^3CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders—0
Automatic Discovery and Optimization of Parts for Image Classification—0
M3ST: Mix at Three Levels for Speech Translation—0
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification—0
Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study—0
Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting—0
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets—0
Machine learning models for Si nanoparticle growth in nonthermal plasma—0
Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing—0
Towards social pattern characterization in egocentric photo-streams—0
Macroscopic Control of Text Generation for Image Captioning—0
Towards Standard Criteria for human evaluation of Chatbots: A Survey—0
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
MAD Speech: Measures of Acoustic Diversity of Speech—0
Automatic Detection and Categorization of Election-Related Tweets—0
Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity—0
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning—0
Towards Summarizing Healthcare Questions in Low-Resource Setting—0
Toward Stable World Models: Measuring and Addressing World Instability in Generative Environments—0
Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm—0
Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation—0
Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization—0
Towards the Synthesis of Non-speech Vocalizations—0
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