awesome-tensorial-neural-networks
github.com/tnbar/awesome-tensorial-neural-networks ↗A thoroughly investigated survey for tensorial neural networks.
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Data Processing with TNNs
- Ballester-Ripoll et al., "TTHRESH: Tensor Compression for Multidimensional Visual Data"Tensor Data Compression and Multi-Task Learning
Error-bounded compression for multidimensional visual tensors
- Ben-Younes et al., "MUTAN: Multimodal Tucker Fusion for Visual Question Answering"Multi-Source Fusion and Multimodal Pooling
Tucker-factorized bilinear interactions
- Do et al., "Compact Trilinear Interaction for Visual Question Answering"Multi-Source Fusion and Multimodal Pooling
Compact trilinear interaction via structured tensor factorization
- Fukui et al., "Multimodal Compact Bilinear Pooling"Multi-Source Fusion and Multimodal Pooling
Compact bilinear pooling for vision-language tasks
- Glasser et al., "Expressive Power of Tensor-Network Factorizations for Probabilistic Modeling"Quantum Data and Probabilistic Modeling
Locally purified states for probabilistic modeling
- Han et al., "Unsupervised Generative Modeling Using Matrix Product States"Quantum Data and Probabilistic Modeling
Generative modeling and sampling with MPS
Related Surveys
- Biamonte and Bergholm, "Tensor Networks in a Nutshell"
Quantum TN foundations, diagrams, contraction, and entanglement
- Panagakis et al., "Tensor Methods in Computer Vision and Deep Learning"
Tensor methods for representation learning, vision, and deep learning
- Panagakis et al., "Tensor Methods in Deep Learning"
Broad tensor methods, applications, and quantum-network material
TNN Model Architectures
- Chen et al., "ANTN"Quantum Neural Networks and Circuit Connections
Autoregressive and TN hybrid for many-body simulation
- Cohen and Shashua, "Convolutional Rectifier Networks as Generalized Tensor Decompositions"Quantum Neural Networks and Circuit Connections
Generalized tensor decomposition view of convolutional networks
- Cohen et al., "On the Expressive Power of Deep Learning: A Tensor Analysis"Quantum Neural Networks and Circuit Connections
CP versus hierarchical tensor decompositions for depth separation
- Ganahl et al., "Density Matrix Renormalization Group with Tensor Processing Units"Scientific Machine Learning
TPU-accelerated DMRG and tensor computation
- Hayashi et al., "Einconv: Exploring Unexplored Tensor Network Decompositions for CNNs"Tensorial CNNs and Compact Vision Models
General tensor-diagram search space for convolution
- Hua et al., "High-Order Pooling for Graph Neural Networks with Tensor Decomposition"Tensorial Graph Neural Networks
High-order nonlinear graph interactions through tensor pooling
Training and Model Lifecycle
- Deng et al., "TIE: Energy-Efficient Tensor Train-Based Inference Engine"Hardware, Contraction, and Tensor Computation
Hardware inference engine for TT layers
- Li et al., "Evolutionary Topology Search for Tensor Network Decomposition"Rank and Tensor-Network Structure Search
Evolutionary search over TN topologies
- Li et al., "Solving Tensor Network Structure Search with Fewer Evaluations"Rank and Tensor-Network Structure Search
Alternating local enumeration for efficient structure search
- Meirom et al., "Optimizing Tensor Network Contraction Using Reinforcement Learning"Hardware, Contraction, and Tensor Computation
RL-based contraction-order optimization
- Memmel et al., "Position: Tensor Networks Are a Valuable Asset for Green AI"Hardware, Contraction, and Tensor Computation
Efficiency- and deployment-aware evaluation of TN models
- Pan et al., "A Unified Weight Initialization Paradigm for TCNNs"Stable Training and Initialization
Variance-preserving initialization across tensor formats
Cross-Cutting Insights
- Falco et al., "Geometric Structures in Tensor Representations"Reviewer-Requested Foundations
Preprint
- Ghadiri et al., "Approximately Optimal Core Shapes for Tensor Decompositions"Reviewer-Requested Foundations
ICML
- Gray and Chan, "Hyperoptimized Approximate Contraction of Tensor Networks with Arbitrary Geometry"Reviewer-Requested Foundations
Physical Review X
- Hameed and Rabusseau, "Efficient Probabilistic Tensor Networks"Reviewer-Requested Foundations
Preprint
- Hong et al., "Generalized Canonical Polyadic Tensor Decomposition"Reviewer-Requested Foundations
SIAM Review
- Li et al., "Permutation Search of Tensor Network Structures via Local Sampling"Reviewer-Requested Foundations
ICML
Interpretability and Analysis
- Hamdi and Angryk, "Interpretable Feature Learning of Graphs Using Tensor Decomposition"
Tensor-factorized latent structure in graph data
- Heidari and Rabusseau, "TN-SHAP-G"
Graph-aligned TN surrogate for exact first- and higher-order Shapley interactions
- Pareja Monturiol et al., "Tensorization of Neural Networks for Improved Privacy and Interpretability"
Black-box MPS construction for privacy and interpretability
- Puljak et al., "Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC"
Interpretable MPS representations for anomaly detection
- Varshneya et al., "Interpretable Tensor Fusion"
Interpretable multimodal tensor interactions
Toolboxes
- ITensorQuantum and Tensor-Network Simulation
MPS/MPO and general TN simulation
- lambeqQuantum and Tensor-Network Simulation
Quantum natural language processing
- Scikit-TTBasic Tensor Operations
TT solvers, data-driven methods, and model construction
- TedNetDeep-Model Implementations
CP, BTT, Tucker-2, TT, and TR neural layers
- TeD-QQuantum and Tensor-Network Simulation
Differentiable quantum machine learning and TN simulation
- TenDeC++Basic Tensor Operations
CP, Tucker, t-SVD, and TT decomposition
Showing a sample of 99 resources. View the full list on GitHub →