Tensor Networks
59 papers with code • 0 benchmarks • 0 datasets
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Convolutions Through the Lens of Tensor Networks
Despite their simple intuition, convolutions are more tedious to analyze than dense layers, which complicates the generalization of theoretical and algorithmic ideas.
Distributive Pre-Training of Generative Modeling Using Matrix-Product States
Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning.
Machine learning with tree tensor networks, CP rank constraints, and tensor dropout
As suggested in [arXiv:2205. 15296] in the context of quantum many-body physics, computation costs can be further substantially reduced by imposing constraints on the canonical polyadic (CP) rank of the tensors in such networks.
Combining Monte Carlo and Tensor-network Methods for Partial Differential Equations via Sketching
In this paper, we propose a general framework for solving high-dimensional partial differential equations with tensor networks.
Compressing neural network by tensor network with exponentially fewer variational parameters
Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters.
Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning
In this paper, we show that by combining a neural-symbolic system with methods from continual learning, LTN can obtain a higher level of accuracy when addressing non-monotonic reasoning tasks.
Tensorizing flows: a tool for variational inference
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples.
Adaptively Topological Tensor Network for Multi-view Subspace Clustering
Therefore, a pre-defined tensor decomposition may not fully exploit low rank information for a certain dataset, resulting in sub-optimal multi-view clustering performance.
Application of quantum-inspired generative models to small molecular datasets
Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-term devices.
Linear to multi-linear algebra and systems using tensors
In particular, with the help of a special form of tensor contracted product, known as the Einstein Product and its properties, many of the known concepts from Linear Algebra could be extended to a multi-linear setting.
