9 citations · 20 across the 7 of their papers we have counts for
6 papers · 1 filter
MTC: Multiresolution Tensor Completion from Partial and Coarse Observations
Chaoqi Yang, Navjot Singh, Cao Xiao +3
Existing tensor completion formulation mostly relies on partial observations from a single tensor. However, tensors extracted from real-world data are often more complex due to: (i…
Fast and Accurate Randomized Algorithms for Low-rank Tensor Decompositions
Linjian Ma, Edgar Solomonik
Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The widely used alternating least squares (ALS) method, which solves a sequence of over-determine…
On Stability of Tensor Networks and Canonical Forms
Yifan Zhang, Edgar Solomonik
Tensor networks such as matrix product states (MPS) and projected entangled pair states (PEPS) are commonly used to approximate quantum systems. These networks are optimized in met…
Derivation and Analysis of Fast Bilinear Algorithms for Convolution
Caleb Ju, Edgar Solomonik
The prevalence of convolution in applications within signal processing, deep neural networks, and numerical solvers has motivated the development of numerous fast convolution algor…
Comparison of Accuracy and Scalability of Gauss-Newton and Alternating Least Squares for CP Decomposition
Navjot Singh, Linjian Ma, Hongru Yang +1
Alternating least squares is the most widely used algorithm for CP tensor decomposition. However, alternating least squares may exhibit slow or no convergence, especially when high…
Accelerating Alternating Least Squares for Tensor Decomposition by Pairwise Perturbation
Linjian Ma, Edgar Solomonik
The alternating least squares algorithm for CP and Tucker decomposition is dominated in cost by the tensor contractions necessary to set up the quadratic optimization subproblems.…