All Relations between representation and matrix compartment

Reference Sentence Publish Date Extraction Date Species
Jiao Wei, Can Tong, Bingxue Wu, Qiang He, Shouliang Qi, Yudong Yao, Yueyang Ten. An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation. IEEE transactions on neural networks and learning systems vol PP issue 2022 35767485 nonnegative matrix factorization (nmf) has been widely used to learn low-dimensional representations of data. 2022-06-29 2022-07-04 Not clear
Kevin B Dsouza, Alexandra Maslova, Ediem Al-Jibury, Matthias Merkenschlager, Vijay K Bhargava, Maxwell W Libbrech. Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation. Nature communications vol 13 issue 1 2022 35764630 we find that these representations contain all the information needed to recreate the observed hi-c matrix with high accuracy, outperforming existing methods. 2022-06-28 2022-07-04 Not clear
Haojiang Tan, Sichao Qiu, Jun Wang, Guoxian Yu, Wei Guo, Maozu Gu. Weighted Deep Factorizing Heterogeneous Molecular Network for Genome-Phenome Association Prediction. Methods (San Diego, Calif.) vol issue 2022 35690250 wdgpa firstly assigns weights to inter/intra-relational data matrices and attribute data matrices, and performs deep matrix factorization on these matrices of heterogeneous network in a cooperative manner to obtain the nonlinear representations of different nodes. 2022-06-11 2022-06-13 Not clear
Kentaro Hino, Yuki Kurashig. Matrix Product State Formulation of the MCTDH Theory in Local Mode Representations for Anharmonic Potentials. Journal of chemical theory and computation vol issue 2022 35606892 matrix product state formulation of the mctdh theory in local mode representations for anharmonic potentials. 2022-05-23 2022-06-13 Not clear
Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akat. Learning Graph Embeddings for Open World Compositional Zero-Shot Learning. IEEE transactions on pattern analysis and machine intelligence vol PP issue 2022 35353693 second, since not all unseen compositions are equally feasible, and less feasible ones may damage the learned representations, co-cge estimates a feasibility score for each unseen composition, using the scores as margins in a cosine similarity-based loss and as weights in the adjacency matrix of the graphs. 2022-03-30 2022-04-14 Not clear
Yifan Shang, Xiucai Ye, Yasunori Futamura, Liang Yu, Tetsuya Sakura. Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving. Briefings in bioinformatics vol issue 2022 35262678 then mccdti adopts matrix completion scheme for dtis prediction based on drug and target representations. 2022-03-09 2022-04-14 Not clear
Qing Yang, Jun Chen, Najla Al-Nabha. Data representation using robust nonnegative matrix factorization for edge computing. Mathematical biosciences and engineering : MBE vol 19 issue 2 2022 35135245 although it can learn parts-based data representations, existing nmf-based algorithms fail to integrate local and global structures of data to steer matrix factorization. 2022-02-09 2022-02-10 Not clear
Qing Yang, Jun Chen, Najla Al-Nabha. Data representation using robust nonnegative matrix factorization for edge computing. Mathematical biosciences and engineering : MBE vol 19 issue 2 2022 35135245 to solve such an issue, we propose a novel semi-supervised nmf approach via joint graph regularization and constraint propagation for edge computing, called robust constrained nonnegative matrix factorization (rcnmf), which learns robust discriminative representations by leveraging the power of both l2, 1-norm nmf and constraint propagation. 2022-02-09 2022-02-10 Not clear
Gan Sun, Yang Cong, Yulun Zhang, Guoshuai Zhao, Yun F. Continual Multiview Task Learning via Deep Matrix Factorization. IEEE transactions on neural networks and learning systems vol 32 issue 1 2021 32175877 more specifically, as a new multiview task arrives, dcmvtl first adopts a deep matrix factorization technique to capture hidden and hierarchical representations for this new coming multiview task while accumulating the fresh multiview knowledge in a layerwise manner. 2022-01-28 2022-01-13 Not clear
Beno\\xc3\\xaet Tuybens, Jacopo De Nardis, Jutho Haegeman, Frank Verstraet. Variational Optimization of Continuous Matrix Product States. Physical review letters vol 128 issue 2 2022 35089726 just as matrix product states represent ground states of one-dimensional quantum spin systems faithfully, continuous matrix product states (cmps) provide faithful representations of the vacuum of interacting field theories in one spatial dimension. 2022-01-28 2022-02-06 Not clear
Ping Xuan, Bingxu Chen, Tiangang Zhang, Yan Yan. Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning. IEEE/ACM transactions on computational biology and bioinformatics vol 18 issue 6 2021 32340959 we propose a network representation learning method based on matrix factorisation to learn low-dimensional vector representations of drug and protein nodes. 2022-01-26 2022-01-13 Not clear
M F Mickevich, Diana Lipscom. PARSIMONY AND THE CHOICE BETWEEN DIFFERENT TRANSFORMATIONS FOR THE SAME CHARACTER SET. Cladistics : the international journal of the Willi Hennig Society vol 7 issue 2 2021 34929943 nearest neighbor networks are graphical representations of the nearest neighbor matrix. 2021-12-21 2022-01-13 Not clear
Chihiro Watanabe, Taiji Suzuk. Deep two-way matrix reordering for relational data analysis. Neural networks : the official journal of the International Neural Network Society vol 146 issue 2021 34920268 most existing matrix reordering techniques share the common processes of extracting some feature representations from an observed matrix in a predefined manner, and applying matrix reordering based on it. 2021-12-17 2022-01-13 Not clear
Ping Xuan, Bingxu Chen, Tiangang Zhang, Yan Yan. Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning. IEEE/ACM transactions on computational biology and bioinformatics vol 18 issue 6 2021 32340959 we propose a network representation learning method based on matrix factorisation to learn low-dimensional vector representations of drug and protein nodes. 2021-12-10 2022-01-13 Not clear
Yesen Sun, Le Ou-Yang, Dao-Qing Da. WMLRR: A Weighted Multi-View Low Rank Representation to Identify Cancer Subtypes From Multiple Types of Omics Data. IEEE/ACM transactions on computational biology and bioinformatics vol 18 issue 6 2021 33656995 given a group of patients described by multiple omics data matrices, we first learn a unified affinity matrix which encodes the similarities among patients by exploring the sparsity-consistent low-rank representations from the joint decompositions of multiple omics data matrices. 2021-12-10 2022-01-13 Not clear
A Xenos, N Malod-Dognin, S Milinkovi\\xc4\\x87, N Pr\\xc5\\xbeul. Linear functional organization of the omic embedding space. Bioinformatics (Oxford, England) vol issue 2021 34213534 recently, it has been shown that neural networks used to obtain vectorial representations (embeddings) are implicitly factorizing a mutual information matrix, called positive pointwise mutual information (ppmi) matrix. 2021-11-09 2022-01-13 Not clear
Jinhao Zhang, Zehua Zhang, Lianrong Pu, Jijun Tang, Fei Gu. AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides. IEEE/ACM transactions on computational biology and bioinformatics vol 18 issue 5 2021 31985437 most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. 2021-11-02 2022-01-13 Not clear
Qing Nie, Frederic Y M Wan, Yong-Tao Zhang, Xin-Feng Li. Compact integration factor methods in high spatial dimensions. Journal of computational physics vol 227 issue 10 2021 19809596 for example, a two-dimensional system of n \xc3\x97 n spatial points, the exponential matrix is of a size of n(2) \xc3\x97 n(2) based on direct representations. 2021-10-20 2022-01-12 Not clear
Sina Mansour L, Ye Tian, B T Thomas Yeo, Vanessa Cropley, Andrew Zalesk. High-resolution connectomic fingerprints: Mapping neural identity and behavior. NeuroImage vol 229 issue 2021 33422711 using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. 2021-10-13 2022-01-13 Not clear
Francisco Galuppo Azevedo, Fabricio Mura. Evaluating the state-of-the-art in mapping research spaces: A Brazilian case study. PloS one vol 16 issue 3 2021 33735233 two recent works propose methods for creating research maps from scientists' publication records: by using a frequentist approach to create a transition probability matrix; and by learning embeddings (vector representations). 2021-10-12 2022-01-13 Not clear
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