Low rank tensor completion for multiway visual data
Tensor completion recovers missing entries of multiway data. The missing of entries could
often be caused during the data acquisition and transformation. In this paper, we provide an …
often be caused during the data acquisition and transformation. In this paper, we provide an …
Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
Subquadratic kronecker regression with applications to tensor decomposition
Kronecker regression is a highly-structured least squares problem $\min_ {\mathbf
{x}}\lVert\mathbf {K}\mathbf {x}-\mathbf {b}\rVert_ {2}^ 2$, where the design matrix $\mathbf …
{x}}\lVert\mathbf {K}\mathbf {x}-\mathbf {b}\rVert_ {2}^ 2$, where the design matrix $\mathbf …
Learning representations from imperfect time series data via tensor rank regularization
There has been an increased interest in multimodal language processing including
multimodal dialog, question answering, sentiment analysis, and speech recognition …
multimodal dialog, question answering, sentiment analysis, and speech recognition …
On Riemannian optimization over positive definite matrices with the Bures-Wasserstein geometry
In this paper, we comparatively analyze the Bures-Wasserstein (BW) geometry with the
popular Affine-Invariant (AI) geometry for Riemannian optimization on the symmetric positive …
popular Affine-Invariant (AI) geometry for Riemannian optimization on the symmetric positive …
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
H Kasai, P Jawanpuria… - … conference on machine …, 2019 - proceedings.mlr.press
Adaptive stochastic gradient algorithms in the Euclidean space have attracted much
attention lately. Such explorations on Riemannian manifolds, on the other hand, are …
attention lately. Such explorations on Riemannian manifolds, on the other hand, are …
Riemannian Hamiltonian methods for min-max optimization on manifolds
In this paper, we study min-max optimization problems on Riemannian manifolds. We
introduce a Riemannian Hamiltonian function, minimization of which serves as a proxy for …
introduce a Riemannian Hamiltonian function, minimization of which serves as a proxy for …
Compressed tensor completion: A robust technique for fast and efficient data reconstruction in wireless sensor networks
In recent times, Compressive sensing (CS) based data collection has become an essential
technique for Wireless Sensor Networks (WSN) because of its benefits of low data …
technique for Wireless Sensor Networks (WSN) because of its benefits of low data …
Effective tensor completion via element-wise weighted low-rank tensor train with overlapping ket augmentation
Tensor completion methods based on the tensor train (TT) have the issues of inaccurate
weight assignment and ineffective tensor augmentation pre-processing. In this work, we …
weight assignment and ineffective tensor augmentation pre-processing. In this work, we …
McTorch, a manifold optimization library for deep learning
In this paper, we introduce McTorch, a manifold optimization library for deep learning that
extends PyTorch. It aims to lower the barrier for users wishing to use manifold constraints in …
extends PyTorch. It aims to lower the barrier for users wishing to use manifold constraints in …