47 citations · 105 across the 21 of their papers we have counts for
6 papers · 1 filter
Matrix Product Operator Restricted Boltzmann Machines
Cong Chen, Kim Batselier, Ching-Yun Ko +1
A restricted Boltzmann machine (RBM) learns a probability distribution over its input samples and has numerous uses like dimensionality reduction, classification and generative mod…
Deep Compression of Sum-Product Networks on Tensor Networks
Ching-Yun Ko, Cong Chen, Yuke Zhang +2
Sum-product networks (SPNs) represent an emerging class of neural networks with clear probabilistic semantics and superior inference speed over graphical models. This work reveals…
The trouble with tensor ring decompositions
Kim Batselier
The tensor train decomposition decomposes a tensor into a "train" of 3-way tensors that are interconnected through the summation of auxiliary indices. The decomposition is stable,…
Multidimensional Realization Theory and Polynomial System Solving
Philippe Dreesen, Kim Batselier, Bart De Moor
Multidimensional systems are becoming increasingly important as they provide a promising tool for estimation, simulation and control, while going beyond the traditional setting of…
A Support Tensor Train Machine
Cong Chen, Kim Batselier, Ching-Yun Ko +1
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilize…
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
Ching-Yun Ko, Kim Batselier, Wenjian Yu +1
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is modeled as a low-rank tensor train, where we use the known tensor entries and their…