main webpage W Topic Machine Learning Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems 2023 Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot m… Article Open Machine Learning Open Article Page

Machine Learning

Study of algorithms that improve automatically through experience Machine learning ( ML ) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page

Exploring foci of: Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems 2023 Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graph… Open Article Page

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Explore Machine Learning W Topic Machine Learning Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems 2023 Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot m… Article Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page