Matrix Completion
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Graph Convolutional Matrix Completion
• tkipf/gae
• 
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings.
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition. Two popular approaches for solving the problem are nuclear-norm-regularized matrix approximation (Candes and Tao, 2009, Mazumder, Hastie and Tibshirani, 2010), and maximum-margin matrix factorization (Srebro, Rennie and Jaakkola, 2005).
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
The main goal of these similar problem formulations is to obtain explicitly or implicitly a decomposition into low-rank matrix plus additive matrices. In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
• SeongokRyu/Graph-neural-networks
The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs.
COMMUNITY DETECTION IMAGE CLASSIFICATION MATRIX COMPLETION NODE CLASSIFICATION
Hybrid Recommender System based on Autoencoders
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach.
SOTA for Collaborative Filtering on Douban
Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising. Its applicability to large datasets has been addressed with online and randomized methods, that reduce the complexity in one of the matrix dimension, but not in both of them.
#2 best model for
Collaborative Filtering on Netflix
COLLABORATIVE FILTERING DICTIONARY LEARNING MATRIX COMPLETION
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs.
#3 best model for
Collaborative Filtering on Flixster
Generalized Low Rank Models
• madeleineudell/LowRankModels.jl
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.
Training Complex Models with Multi-Task Weak Supervision
As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used.
VIGAN: Missing View Imputation with Generative Adversarial Networks
In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. Especially, when certain samples miss an entire view of data, it creates the missing view problem.