Tensor Methods in Computer Vision and Deep Learning
暂无分享,去创建一个
Stefanos Zafeiriou | Anima Anandkumar | Jean Kossaifi | Yannis Panagakis | Mihalis A. Nicolaou | Grigorios G. Chrysos | James Oldfield | Anima Anandkumar | Jean Kossaifi | S. Zafeiriou | Yannis Panagakis | James Oldfield | M. Nicolaou
[1] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[2] F. L. Hitchcock. The Expression of a Tensor or a Polyadic as a Sum of Products , 1927 .
[3] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[4] M. Stone. The Generalized Weierstrass Approximation Theorem , 1948 .
[5] R. Bellman,et al. V. Adaptive Control Processes , 1964 .
[6] L. Tucker,et al. Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.
[7] J. Chang,et al. Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .
[8] Richard A. Harshman,et al. Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .
[9] A. G. Ivakhnenko,et al. Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..
[10] D. Cantor,et al. A new algorithm for factoring polynomials over finite fields , 1981 .
[11] Dimitri P. Bertsekas,et al. Constrained Optimization and Lagrange Multiplier Methods , 1982 .
[12] Lajos Rónyai,et al. Factoring polynomials over finite fields , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[13] Johan Håstad,et al. Tensor Rank is NP-Complete , 1989, ICALP.
[14] Joydeep Ghosh,et al. The pi-sigma network: an efficient higher-order neural network for pattern classification and function approximation , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[15] Balas K. Natarajan,et al. Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..
[16] S. Ullman,et al. Generalization to Novel Images in Upright and Inverted Faces , 1993, Perception.
[17] S. Rommer,et al. CLASS OF ANSATZ WAVE FUNCTIONS FOR ONE-DIMENSIONAL SPIN SYSTEMS AND THEIR RELATION TO THE DENSITY MATRIX RENORMALIZATION GROUP , 1997 .
[18] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[19] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[20] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[21] John A. Richards,et al. Remote Sensing Digital Image Analysis: An Introduction , 1999 .
[22] Rich Caruana,et al. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping , 2000, NIPS.
[23] Joos Vandewalle,et al. A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..
[24] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[25] H. Kiers. Towards a standardized notation and terminology in multiway analysis , 2000 .
[26] Joshua B. Tenenbaum,et al. Separating Style and Content with Bilinear Models , 2000, Neural Computation.
[27] Demetri Terzopoulos,et al. Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.
[28] M. Alex O. Vasilescu. Human motion signatures: analysis, synthesis, recognition , 2002, Object recognition supported by user interaction for service robots.
[29] Terence Sim,et al. The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.
[30] Azriel Rosenfeld,et al. Accurate dense optical flow estimation using adaptive structure tensors and a parametric model , 2003, IEEE Trans. Image Process..
[31] Sung-Kwun Oh,et al. Polynomial neural networks architecture: analysis and design , 2003, Comput. Electr. Eng..
[32] Chien-Kuo Li. A Sigma-Pi-Sigma Neural Network (SPSNN) , 2004, Neural Processing Letters.
[33] Rasmus Bro,et al. Multi-way Analysis with Applications in the Chemical Sciences , 2004 .
[34] Rama Chellappa,et al. Appearance-based tracking and recognition using the 3D trilinear tensor , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[35] Tamir Hazan,et al. Non-negative tensor factorization with applications to statistics and computer vision , 2005, ICML.
[36] Hanspeter Pfister,et al. Face transfer with multilinear models , 2005, ACM Trans. Graph..
[37] Kohei Inoue,et al. DSVD: a tensor-based image compression and recognition method , 2005, 2005 IEEE International Symposium on Circuits and Systems.
[38] Ahmed M. Elgammal,et al. Towards Scalable View-Invariant Gait Recognition: Multilinear Analysis for Gait , 2005, AVBPA.
[39] David E. Booth,et al. Multi-Way Analysis: Applications in the Chemical Sciences , 2005, Technometrics.
[40] Luiz Velho,et al. Expression Transfer between Photographs through Multilinear AAM's , 2006, 2006 19th Brazilian Symposium on Computer Graphics and Image Processing.
[41] Haiping Lu,et al. Multilinear Principal Component Analysis of Tensor Objects for Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[42] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[43] Mohan M. Trivedi,et al. A Regression Model in TensorPCA Subspace for Face Image Super-resolution Reconstruction , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[44] Dong Xu,et al. Multilinear Discriminant Analysis for Face Recognition , 2007, IEEE Transactions on Image Processing.
[45] M. Alex O. Vasilescu,et al. Multilinear Projection for Appearance-Based Recognition in the Tensor Framework , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[46] Haiping Lu,et al. Uncorrelated Multilinear Discriminant Analysis with Regularization for Gait Recognition , 2007, 2007 Biometrics Symposium.
[47] Stephen Lin,et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Xuelong Li,et al. General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] Haiping Lu,et al. Boosting LDA with Regularization on MPCA Features for Gait Recognition , 2007, 2007 Biometrics Symposium.
[50] Haiping Lu,et al. MPCA: Multilinear Principal Component Analysis of Tensor Objects , 2008, IEEE Transactions on Neural Networks.
[51] Lieven De Lathauwer,et al. Decompositions of a Higher-Order Tensor in Block Terms - Part II: Definitions and Uniqueness , 2008, SIAM J. Matrix Anal. Appl..
[52] Takeo Kanade,et al. Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[53] Constantine Kotropoulos,et al. Music Genre Classification Using Locality Preserving Non-Negative Tensor Factorization and Sparse Representations , 2009, ISMIR.
[54] Haiping Lu,et al. Boosting Discriminant Learners for Gait Recognition Using MPCA Features , 2009, EURASIP J. Image Video Process..
[55] Bülent Yener,et al. Unsupervised Multiway Data Analysis: A Literature Survey , 2009, IEEE Transactions on Knowledge and Data Engineering.
[56] Jieping Ye,et al. Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[58] Rajat Raina,et al. Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.
[59] Andrzej Cichocki,et al. Nonnegative Matrix and Tensor Factorization T , 2007 .
[60] Hans-Peter Seidel,et al. Multilinear pose and body shape estimation of dressed subjects from image sets , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[61] Constantine Kotropoulos,et al. Non-Negative Multilinear Principal Component Analysis of Auditory Temporal Modulations for Music Genre Classification , 2010, IEEE Transactions on Audio, Speech, and Language Processing.
[62] Sanjoy Dasgupta,et al. Adaptive Control Processes , 2010, Encyclopedia of Machine Learning and Data Mining.
[63] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[64] Yin Li,et al. Optimum Subspace Learning and Error Correction for Tensors , 2010, ECCV.
[65] Guillermo Sapiro,et al. Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..
[66] Steffen Rendle,et al. Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.
[67] Rama Chellappa,et al. A Fast Bilinear Structure from Motion Algorithm Using a Video Sequence and Inertial Sensors , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[68] Xiaoqin Zhang,et al. Visual tracking via dynamic tensor analysis with mean update , 2011, Neurocomputing.
[69] Damian Garside,et al. ‘Shield’ , 2011, Encyclopedic Dictionary of Archaeology.
[70] Luca Maria Gambardella,et al. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification , 2022 .
[71] Ivan Oseledets,et al. Tensor-Train Decomposition , 2011, SIAM J. Sci. Comput..
[72] J. Landsberg. Tensors: Geometry and Applications , 2011 .
[73] Ben Taskar,et al. Regularized Tensor Factorization for Multi-Modality Medical Image Classification , 2011, MICCAI.
[74] Haiping Lu,et al. A survey of multilinear subspace learning for tensor data , 2011, Pattern Recognit..
[75] Tonio Ball,et al. Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach , 2011, NIPS.
[76] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[77] B. Recht,et al. Tensor completion and low-n-rank tensor recovery via convex optimization , 2011 .
[78] Fernando De la Torre,et al. A Least-Squares Framework for Component Analysis , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[79] Kishore Kumar Naraparaju,et al. A note on tensor chain approximation , 2012, Comput. Vis. Sci..
[80] Dacheng Tao,et al. Slow Feature Analysis for Human Action Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[81] Caroline Fossati,et al. Denoising of Hyperspectral Images Using the PARAFAC Model and Statistical Performance Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[82] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[83] Yong Fang,et al. 2D sparse signal recovery via 2D orthogonal matching pursuit , 2012, Science China Information Sciences.
[84] Shinichi Nakajima,et al. Perfect Dimensionality Recovery by Variational Bayesian PCA , 2012, NIPS.
[85] Weiwei Guo,et al. Tensor Learning for Regression , 2012, IEEE Transactions on Image Processing.
[86] Zicheng Liu,et al. Tensor-Based Human Body Modeling , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[87] Naotaka Fujii,et al. Higher Order Partial Least Squares (HOPLS): A Generalized Multilinear Regression Method , 2013, IEEE Trans. Pattern Anal. Mach. Intell..
[88] Geoffrey E. Hinton,et al. Tensor Analyzers , 2013, ICML.
[89] Yann LeCun,et al. Regularization of Neural Networks using DropConnect , 2013, ICML.
[90] Massimiliano Pontil,et al. Multilinear Multitask Learning , 2013, ICML.
[91] Vincent Lepetit,et al. Learning Separable Filters , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[92] Martin Kleinsteuber,et al. Separable Dictionary Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[93] Liangpei Zhang,et al. Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[94] Christopher J. Hillar,et al. Most Tensor Problems Are NP-Hard , 2009, JACM.
[95] Martin J. Mohlenkamp. Musings on multilinear fitting , 2013 .
[96] Hongtu Zhu,et al. Tensor Regression with Applications in Neuroimaging Data Analysis , 2012, Journal of the American Statistical Association.
[97] Haiping Lu,et al. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data , 2013 .
[98] Xianjun Shi,et al. A Fixed Point Iterative Method for Low $n$-Rank Tensor Pursuit , 2013, IEEE Transactions on Signal Processing.
[99] Andrew Zisserman,et al. Speeding up Convolutional Neural Networks with Low Rank Expansions , 2014, BMVC.
[100] René Vidal,et al. Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing , 2014, ICML.
[101] Pierre Comon,et al. Tensors : A brief introduction , 2014, IEEE Signal Processing Magazine.
[102] Dan Schonfeld,et al. Multilinear Discriminant Analysis for Higher-Order Tensor Data Classification , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[103] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[104] David B. Dunson,et al. Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors , 2014, ICML.
[105] Lieven De Lathauwer,et al. Numerical Solution of Bivariate and Polyanalytic Polynomial Systems , 2014, SIAM J. Numer. Anal..
[106] Nikos D. Sidiropoulos,et al. Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200x , 2014, SDM.
[107] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[108] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[109] Hong Cheng,et al. Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion , 2014, NIPS.
[110] Anton van den Hengel,et al. Semidefinite Programming , 2014, Computer Vision, A Reference Guide.
[111] Donald Goldfarb,et al. Robust Low-Rank Tensor Recovery: Models and Algorithms , 2013, SIAM J. Matrix Anal. Appl..
[112] Anima Anandkumar,et al. Tensor decompositions for learning latent variable models , 2012, J. Mach. Learn. Res..
[113] Philip S. Yu,et al. DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages , 2014, SDM.
[114] Soo-Chang Pei,et al. 2D sparse dictionary learning via tensor decomposition , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[115] René Vidal,et al. Global Optimality in Tensor Factorization, Deep Learning, and Beyond , 2015, ArXiv.
[116] John Wright,et al. Provable Models for Robust Low-Rank Tensor Completion , 2015 .
[117] Rama Chellappa,et al. Compositional Dictionaries for Domain Adaptive Face Recognition , 2013, IEEE Transactions on Image Processing.
[118] Zheng Zhang,et al. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.
[119] Ivan V. Oseledets,et al. Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition , 2014, ICLR.
[120] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[121] David Zhang,et al. A Survey of Sparse Representation: Algorithms and Applications , 2015, IEEE Access.
[122] Johan Schoukens,et al. Decoupling Multivariate Polynomials Using First-Order Information and Tensor Decompositions , 2014, SIAM J. Matrix Anal. Appl..
[123] Andrzej Cichocki,et al. Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis , 2014, IEEE Signal Processing Magazine.
[124] Yuan Xie,et al. A New Low-Rank Tensor Model for Video Completion , 2015, ArXiv.
[125] Liqing Zhang,et al. Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[126] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[127] Pierre Comon,et al. A Polynomial Formulation for Joint Decomposition of Symmetric Tensors of Different Orders , 2015, LVA/ICA.
[128] Jürgen Schmidhuber,et al. Highway Networks , 2015, ArXiv.
[129] Alexander Novikov,et al. Tensorizing Neural Networks , 2015, NIPS.
[130] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[131] Gaël Varoquaux,et al. Dictionary Learning for Massive Matrix Factorization , 2016, ICML.
[132] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[133] Naonori Ueda,et al. Higher-Order Factorization Machines , 2016, NIPS.
[134] Eunhyeok Park,et al. Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications , 2015, ICLR.
[135] Hachem Kadri,et al. Low-Rank Regression with Tensor Responses , 2016, NIPS.
[136] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[137] Anand D. Sarwate,et al. Minimax lower bounds for Kronecker-structured dictionary learning , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[138] Amnon Shashua,et al. Convolutional Rectifier Networks as Generalized Tensor Decompositions , 2016, ICML.
[139] Liqing Zhang,et al. Tensor Ring Decomposition , 2016, ArXiv.
[140] Jia Deng,et al. Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.
[141] Rose Yu,et al. Learning from Multiway Data: Simple and Efficient Tensor Regression , 2016, ICML.
[142] Alexander Novikov,et al. Ultimate tensorization: compressing convolutional and FC layers alike , 2016, ArXiv.
[143] Andrzej Cichocki,et al. Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data , 2015, Proceedings of the IEEE.
[144] Anima Anandkumar,et al. Tensor Contractions with Extended BLAS Kernels on CPU and GPU , 2016, 2016 IEEE 23rd International Conference on High Performance Computing (HiPC).
[145] Tomaso Poggio,et al. Learning Functions: When Is Deep Better Than Shallow , 2016, 1603.00988.
[146] Stefanos Zafeiriou,et al. Learning the Multilinear Structure of Visual Data , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[147] David A. Patterson,et al. In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[148] Masashi Sugiyama,et al. Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives , 2017, Found. Trends Mach. Learn..
[149] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[150] Yongxin Yang,et al. Deep Multi-task Representation Learning: A Tensor Factorisation Approach , 2016, ICLR.
[151] Joelle Pineau,et al. Tensor Regression Networks with various Low-Rank Tensor Approximations , 2017, ArXiv.
[152] Wensheng Zhang,et al. The Twist Tensor Nuclear Norm for Video Completion , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[153] Anima Anandkumar,et al. Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods , 2017 .
[154] Haiping Lu,et al. Multilinear Regression for Embedded Feature Selection with Application to fMRI Analysis , 2017, AAAI.
[155] Seung-Ik Lee,et al. CP-decomposition with Tensor Power Method for Convolutional Neural Networks compression , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).
[156] Gangyao Kuang,et al. Hyperspectral Image Restoration Using Low-Rank Tensor Recovery , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[157] Anima Anandkumar,et al. Tensor Contraction Layers for Parsimonious Deep Nets , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[158] Nikos D. Sidiropoulos,et al. Tensors for Data Mining and Data Fusion , 2016, ACM Trans. Intell. Syst. Technol..
[159] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[160] Stefanos Zafeiriou,et al. Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[161] Maja Pantic,et al. Fast and Exact Newton and Bidirectional Fitting of Active Appearance Models , 2017, IEEE Transactions on Image Processing.
[162] Sudhish N. George,et al. Video Inpainting Based on Re-weighted Tensor Decomposition , 2017, CVIP.
[163] Yi Yang,et al. More is Less: A More Complicated Network with Less Inference Complexity , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[164] Nikos D. Sidiropoulos,et al. Tensor Decomposition for Signal Processing and Machine Learning , 2016, IEEE Transactions on Signal Processing.
[165] Li Chen,et al. SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression , 2018, KDD.
[166] Antonis Nikitakis,et al. Tensor-Based Classification Models for Hyperspectral Data Analysis , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[167] Edmond Boyer,et al. Multilinear Autoencoder for 3D Face Model Learning , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[168] Anand D. Sarwate,et al. Minimax Lower Bounds on Dictionary Learning for Tensor Data , 2016, IEEE Transactions on Information Theory.
[169] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[170] Abhinav Gupta,et al. Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[171] Evangelia I. Zacharaki,et al. Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data , 2018, Complex..
[172] Xiaoshan Li,et al. Tucker Tensor Regression and Neuroimaging Analysis , 2013, Statistics in biosciences.
[173] Patrick Snape,et al. Disentangling the Modes of Variation in Unlabelled Data , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[174] Amnon Shashua,et al. Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design , 2017, ICLR.
[175] Shuicheng Yan,et al. Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks , 2018, IJCAI.
[176] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[177] Jason D. Lee,et al. On the Power of Over-parametrization in Neural Networks with Quadratic Activation , 2018, ICML.
[178] M. Alex O. Vasilescu,et al. Compositional Hierarchical Tensor Factorization: Representing Hierarchical Intrinsic and Extrinsic Causal Factors , 2019, ArXiv.
[179] Anima Anandkumar,et al. Spectral Learning on Matrices and Tensors , 2019, Found. Trends Mach. Learn..
[180] Silvio Savarese,et al. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[181] Sanjeev Arora,et al. Implicit Regularization in Deep Matrix Factorization , 2019, NeurIPS.
[182] Maja Pantic,et al. TensorLy: Tensor Learning in Python , 2016, J. Mach. Learn. Res..
[183] Adria Ruiz,et al. Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation , 2019, International Journal of Computer Vision.
[184] Chao Li,et al. Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion , 2018, AAAI.
[185] Maja Pantic,et al. T-Net: Parametrizing Fully Convolutional Nets With a Single High-Order Tensor , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[186] Stefanos Zafeiriou,et al. Robust Kronecker Component Analysis , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[187] Jonathan Cheung-Wai Chan,et al. Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[188] Andrzej Cichocki,et al. Automated Multi-Stage Compression of Neural Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[189] Maja Pantic,et al. Valence and Arousal Estimation In-The-Wild with Tensor Methods , 2019, 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019).
[190] Kohei Hayashi,et al. Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks , 2022, NeurIPS.
[191] Timo Aila,et al. A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[192] Panos P. Markopoulos,et al. L1-Norm Tucker Tensor Decomposition , 2019, IEEE Access.
[193] Masashi Sugiyama,et al. Learning Efficient Tensor Representations with Ring-structured Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[194] Ce Zhu,et al. Tensor rank learning in CP decomposition via convolutional neural network , 2019, Signal Process. Image Commun..
[195] Mengjiao Wang,et al. An Adversarial Neuro-Tensorial Approach for Learning Disentangled Representations , 2017, International Journal of Computer Vision.
[196] Adel Javanmard,et al. Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks , 2017, IEEE Transactions on Information Theory.
[197] Maja Pantic,et al. Defensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks , 2019 .
[198] Rafał Zdunek,et al. Image Completion with Hybrid Interpolation in Tensor Representation , 2020 .
[199] Maja Pantic,et al. Efficient N-Dimensional Convolutions via Higher-Order Factorization , 2019, ArXiv.
[200] Peter Lindstrom,et al. TTHRESH: Tensor Compression for Multidimensional Visual Data , 2018, IEEE Transactions on Visualization and Computer Graphics.
[201] Stefanos Zafeiriou,et al. P–nets: Deep Polynomial Neural Networks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[202] Maja Pantic,et al. Speech-Driven Facial Animation Using Polynomial Fusion of Features , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[203] Martin Jaggi,et al. Multi-Head Attention: Collaborate Instead of Concatenate , 2020, ArXiv.
[204] Jan Kautz,et al. Convolutional Tensor-Train LSTM for Spatio-temporal Learning , 2020, NeurIPS.
[205] Anima Anandkumar,et al. Tensor Regression Networks , 2017, J. Mach. Learn. Res..
[206] Andrzej Cichocki,et al. Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network , 2020, ECCV.
[207] E. M. Stoudenmire,et al. The ITensor Software Library for Tensor Network Calculations , 2020, SciPost Physics Codebases.
[208] Raghavendra Selvan,et al. Tensor Networks for Medical Image Classification , 2020, MIDL.
[209] Josef Kittler,et al. A Unified Tensor-based Active Appearance Model , 2019 .
[210] Maja Pantic,et al. Multilinear Latent Conditioning for Generating Unseen Attribute Combinations , 2020, ICML.
[211] Zenglin Xu,et al. Block-term tensor neural networks , 2020, Neural Networks.
[212] Stefanos Zafeiriou,et al. Π-nets: Deep Polynomial Neural Networks , 2020, ArXiv.
[213] Wenqi Lu,et al. High-dimensional Quantile Tensor Regression , 2020 .
[214] Gi Pyo Nam,et al. Adaptive 3D Model-Based Facial Expression Synthesis and Pose Frontalization , 2020, Sensors.
[215] Taiji Suzuki,et al. Understanding Generalization in Deep Learning via Tensor Methods , 2020, AISTATS.
[216] Yannis Panagakis,et al. NAPS: Non-adversarial polynomial synthesis , 2020, Pattern Recognit. Lett..
[217] Alexander Novikov,et al. Tensor Train decomposition on TensorFlow (T3F) , 2018, J. Mach. Learn. Res..
[218] Stefanos Zafeiriou,et al. TESA: Tensor Element Self-Attention via Matricization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[219] Yee Whye Teh,et al. Multiplicative Interactions and Where to Find Them , 2020, ICLR.
[220] Evangelos E. Papalexakis,et al. TensorShield: Tensor-based Defense Against Adversarial Attacks on Images , 2020, ArXiv.
[221] Panos P. Markopoulos,et al. L1-Norm Higher-Order Orthogonal Iterations for Robust Tensor Analysis , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[222] Maja Pantic,et al. Incremental multi-domain learning with network latent tensor factorization , 2019, AAAI.
[223] Anima Anandkumar,et al. Tensor Dropout for Robust Learning. , 2020 .
[224] Stefanos Zafeiriou,et al. Deep Polynomial Neural Networks , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[225] Raphaël M. Jungers,et al. Equivalent Polyadic Decompositions of Matrix Multiplication Tensors , 2019, J. Comput. Appl. Math..