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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 5401–5425 of 9051 papers

TitleStatusHype
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling—0
Autoencoder-based General Purpose Representation Learning for Customer Embedding—0
Autoencoder-Based Framework to Capture Vocabulary Quality in NLP—0
Mask-Guided Matting in the Wild—0
Mask-Guided Portrait Editing with Conditional GANs—0
Toward Understanding the Impact of Staleness in Distributed Machine Learning—0
AutoComPose: Automatic Generation of Pose Transition Descriptions for Composed Pose Retrieval Using Multimodal LLMs—0
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction—0
Autocompletion interfaces make crowd workers slower, but their use promotes response diversity—0
Toward Wireless Localization Using Multiple Reconfigurable Intelligent Surfaces—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
Autocatalytic Sets and RNA Secondary Structure—0
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints—0
Matina: A Large-Scale 73B Token Persian Text Corpus—0
Autobots@LT-EDI-EACL2021: One World, One Family: Hope Speech Detection with BERT Transformer Model—0
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment—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
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