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Self-Supervised Learning for Multimedia Recommendation
IEEE transactions on multimedia, 2023Learning representations for multimedia content is critical for multimedia recommendation. Current representation learning methods roughly fall into two groups: (1) using the historical interactions to create ID embeddings of users and items, and (2 ...
Zhulin Tao +6 more
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Approximation Methods for Supervised Learning [PDF]
Foundations of Computational Mathematics, 2005 Let ź be an unknown Borel measure defined on the space Z := X × Y with X ź źd and Y = [-M,M]. Given a set z of m samples zi =(xi,yi) drawn according to ź, the problem of estimating a regression function fź using these samples is considered. The main focus is to understand what is the rate of approximation, measured either in expectation or probability,
Kerkyacharian, G. +3 more
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2008
Supervised learning accounts for a lot of research activity in machine learning and many supervised learning techniques have found application in the processing of multimedia content. The defining characteristic of supervised learning is the availability of annotated training data. The name invokes the idea of a 'supervisor' that instructs the learning
Cunningham, Pádraig +2 more
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Supervised learning accounts for a lot of research activity in machine learning and many supervised learning techniques have found application in the processing of multimedia content. The defining characteristic of supervised learning is the availability of annotated training data. The name invokes the idea of a 'supervisor' that instructs the learning
Cunningham, Pádraig +2 more
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Deconstructing Denoising Diffusion Models for Self-Supervised Learning
International Conference on Learning RepresentationsIn this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation.
Xinlei Chen +3 more
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Knowledge and Information Systems, 2006
This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM).
Xuelong Li +4 more
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This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM).
Xuelong Li +4 more
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Overview of Supervised Learning
2001The first three examples described in Chapter 1 have several components in common. For each there is a set of variables that might be denoted as inputs, which are measured or preset. These have some influence on one or more outputs. For each example the goal is to use the inputs to predict the values of the outputs.
Jerome H. Friedman +2 more
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Performances in supervised learning
Physica A: Statistical Mechanics and its Applications, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Constrained supervised learning
Journal of Mathematical Psychology, 1992zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Supervision interactions and student learning: how supervision can influence learning
Physiotherapy Theory and Practice, 2019Background/Introduction: Supervision interactions are a central part of clinical education. The researcher explored and described the intricate construction of supervision interactions to better understand the influence thereof on student learning. Objectives: The aim of this study was to explore how Clinical Educators and physiotherapy students use ...
Anna Schmutz, Ilse Meyer, Elize Archer
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Supervised and Semi-supervised Machine Learning Ranking
2007We present a Semi-supervised Machine Learning based ranking model which can automatically learn its parameters using a training set of a few labeled and unlabeled examples composed of queries and relevance judgments on a subset of the document elements.
Vittaut, Jean-Noël, Gallinari, Patrick
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