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Found 8 papers, 2 papers with code
TERM Model: Tensor Ring Mixture Model for Density Estimation
However, a substantial number of potential tensor permutations can lead to a tensor network with the same structure but varying expressive capabilities.
Quantization Aware Factorization for Deep Neural Network Compression
Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks.
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle.
How to Train Unstable Looped Tensor Network
This motivates using a hybrid model of CPD and TKD, a decomposition with multiple Tucker models with small core tensor, known as block term decomposition (BTD).
Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network
Most state of the art deep neural networks are overparameterized and exhibit a high computational cost.
CNN Acceleration by Low-rank Approximation with Quantized Factors
The modern convolutional neural networks although achieve great results in solving complex computer vision tasks still cannot be effectively used in mobile and embedded devices due to the strict requirements for computational complexity, memory and power consumption.
Quadratic Programming Over Ellipsoids (with Applications to Constrained Linear Regression and Tensor Decomposition)
A novel algorithm to solve the quadratic programming problem over ellipsoids is proposed.
Optimization and Control
