| Basis Sharing and Coefficient Pruning for Efficient Neural Network Compression | Neural Networks | 2026 |
| A Novel Approach to Lossless Convolutional Neural Network Compression via Progressive Knowledge Distillation-Incorporated Low-Rank Compression | Neural Networks | 2026 |
| Deep Neural Network Compression via Data-Driven Low-Rank Singular Value Decomposition | Journal of Artificial Intelligence Research | 2026 |
| Adaptive Tucker Decomposition-based Progressive Model Compression for Convolutional Neural Networks | Expert Systems with Applications | 2026 |
Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks  | NeurIPS | 2025 |
Coupled Tensor Decomposition for Compact Network Representation  | TNNLS | 2025 |
Reduced storage direct tensor ring decomposition for convolutional neural networks compression  | Neural Networks | 2025 |
| Co-Exploring Structured Sparsification and Low-Rank Tensor Decomposition for Compact DNNs | TNNLS | 2025 |
| Rope-net: deep convolutional neural network via robust principal component analysis | Machine Learning | 2025 |
| QuaDCNN: Quantized compression of deep CNN based on tensor-train decomposition with automatic rank determination | Neurocomputing | 2025 |
| An overview of tensor and matrix decomposition methods, applied to deep neural networks | Evolving Systems | 2025 |
| FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training | Arxiv | 2025 |
| Fine-grained hierarchical singular value decomposition for convolutional neural networks compression and acceleration | Neurocomputing | 2025 |
| Unified Framework for Pre-trained Neural Network Compression via Decomposition and Optimized Rank Selection | ECML PKDD | 2025 |
| Low-Rank Compression for IMC Arrays | Arxiv | 2025 |
Enhancing GANs With MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition  | IEEE ACCESS | 2024 |
| Quantization Aware Factorization for Deep Neural Network Compression | JAIR | 2024 |
| J-Tucker: Joint Compression Scheme for Efficient Deployment of Multi-Task Deep Learning Models on Edge Devices | IEEE Network | 2024 |
| 3D-KCPNet: Efficient 3DCNNs based on tensor mapping theory | Neurocomputing | 2024 |
| Fully Tensorized Lightweight ConvLSTM Neural Networks for Hyperspectral Image Classification | TNNLS | 2024 |
| TEC-CNN: Towards Efficient Compressing Convolutional Neural Nets with Low-rank Tensor Decomposition | TOMM | 2024 |
Activation Map Compression through Tensor Decomposition for Deep Learning  | NeurIPS | 2024 |
| Geometry-aware training of factorized layers in tensor Tucker format | NeurIPS | 2024 |
| Robustness of Tensor Decomposition-Based Neural Network Compression | ICIP | 2024 |
| Stable Low-Rank CP Decomposition for Compression of Convolutional Neural Networks Based on Sensitivity | Applied Sciences | 2024 |
| How to Train Your Unstable Looped Tensor Network | JSTSP | 2024 |
Learning Low-Rank Tensor Cores with Probabilistic l0-Regularized Rank Selection for Model Compression  | IJCAI | 2024 |
Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition  | TMLR | 2024 |
Compact Model Training by Low-Rank Projection With Energy Transfer  | TNNLS | 2024 |
| An Accuracy-Preserving Neural Network Compression via Tucker Decomposition | IEEE Transactions on Sustainable Computing | 2024 |
| Convolution Filter Compression via Sparse Linear Combinations of Quantized Basis | TNNLS | 2024 |
Coarse-To-Fine Tensor Trains for Compact Visual Representations  | ICML | 2024 |
| Position: Tensor Networks are a Valuable Asset for Green AI | ICML | 2024 |
Compression-aware Training of Neural Networks using Frank-Wolfe  | Arxiv | 2024 |
| A Practical Approach for Employing Tensor Train Decomposition in Edge Devices | IJPP | 2024 |
| Neural Network Compression Based on Tensor Ring Decomposition | TNNLS | 2024 |
Enhanced network compression through tensor decompositions and pruning  | TNNLS | 2024 |
| Deep Convolutional Neural Network Compression Method: Tensor Ring Decomposition with Variational Bayesian Approach | Neural Processing Letters | 2024 |
| Deep Learning Model Compression With Rank Reduction in Tensor Decomposition | TNNLS | 2023 |
MARS: Masked Automatic Ranks Selection in Tensor Decompositions  | AISTATS | 2023 |
Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor Decomposition  | FPL | 2023 |
SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search  | WACV | 2023 |
| How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression? | ICLR | 2023 |
| Learning Low-Rank Representations for Model Compression | IJCNN | 2023 |
| Tensor shape search for efficient compression of tensorized data and neural networks | Applied Soft Computing | 2023 |
Compressing convolutional neural networks with hierarchical Tucker-2 decomposition  | Applied Soft Computing | 2023 |
| Tensor shape search for efficient compression of tensorized data and neural networks | Applied Soft Computing | 2023 |
| An effective low-rank compression with a joint rank selection followed by a compression-friendly training | Neural Networks | 2023 |
Joint matrix decomposition for deep convolutional neural networks compression  | Neurocomputing | 2023 |
| Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization | Arxiv | 2023 |
Knowledge Transfer via Decomposing Essential Information in Convolutional Neural Networks  | TNNLS | 2022 |
| Compression of Deep Neural Networks based on quantized tensor decomposition to implement on reconfigurable hardware platforms | Neural Networks | 2022 |
| Teacher–student knowledge distillation based on decomposed deep feature representation for intelligent mobile applications | Expert Systems with Applications | 2022 |
| HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks | CVPR | 2022 |
Towards Practical Control of Singular Values of Convolutional Layers  | NeurIPS | 2022 |
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations  | NeurIPS | 2022 |
| Realistic acceleration of neural networks with fine-grained tensor decomposition | Neurocomputing | 2022 |
| BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition | AAAI | 2022 |
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition  | AAAI | 2022 |
Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination  | SIMODS | 2022 |
A novel compact design of convolutional layers with spatial transformation towards lower-rank representation for image classification  | Knowledge-Based Systems | 2022 |
| Deep neural network compression by Tucker decomposition with nonlinear response | Knowledge-Based Systems | 2022 |
| Nested compression of convolutional neural networks with Tucker-2 decomposition | IJCNN | 2022 |
| PSM-nets: Compressing Neural Networks with Product of Sparse Matrices | IJCNN | 2022 |
| PARS: Proxy-Based Automatic Rank Selection for Neural Network Compression via Low-Rank Weight Approximation | Mathematics | 2022 |
| A Design Space Exploration Methodology for Enabling Tensor Train Decomposition in Edge Devices | SAMOS | 2022 |
| Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks | NeurIPS | 2021 |
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition  | NeurIPS | 2021 |
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks  | NeurIPS | 2021 |
Decomposable-Net: Scalable Low-Rank Compression for Neural Networks  | IJCAI | 2021 |
Tensor Regression Networks  | JMLR | 2021 |
Parameter Efficient Dynamic Convolution via Tensor Decomposition  | BMVC | 2021 |
| Nonlinear tensor train format for deep neural network compression | Neural Networks | 2021 |
| Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework | CVPR | 2021 |
Pufferfish: Communication-efficient Models At No Extra Cost  | MLSys | 2021 |
| Learning-based Tensor Decomposition with Adaptive Rank Penalty for CNNs Compression | MIPR | 2021 |
| Deep Convolutional Neural Network Compression via Coupled Tensor Decomposition | JSTSP | 2021 |
| Tensor Reordering for CNN Compression | ICASSP | 2021 |
| Block-term tensor neural networks | Neural Networks | 2020 |
| Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network | ECCV | 2020 |
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer  | CVPR | 2020 |
Few Sample Knowledge Distillation for Efficient Network Compression  | CVPR | 2020 |
| Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation | CVPR | 2020 |
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification  | CVPRW | 2020 |
T-Basis: a Compact Representation for Neural Networks  | ICML | 2020 |
PENNI: Pruned Kernel Sharing for Efficient CNN Inference  | ICML | 2020 |
| A Novel Rank Selection Scheme in Tensor Ring Decomposition Based on Reinforcement Learning for Deep Neural Networks | ICASSP | 2020 |
Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer  | TPAMI | 2019 |
| LTNN: A Layerwise Tensorized Compression of Multilayer Neural Network | TNNLS | 2019 |
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks  | NeurIPS | 2019 |
Efficient Neural Network Compression  | CVPR | 2019 |
| ADA-Tucker: Compressing deep neural networks via adaptive dimension adjustment tucker decomposition | Neural Networks | 2019 |
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks  | ICLR | 2019 |
Learning Filter Basis for Convolutional Neural Network Compression  | ICCV | 2019 |
Automated Multi-Stage Compression of Neural Networks  | ICCVW | 2019 |
| Compressing Deep Models using Multi Tensor Train Decomposition | ICCAIS | 2019 |
| Compressing Fully Connected Layers using Kronecker Tensor Decomposition | ICCSNT | 2019 |
Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling  | OpenReview | 2019 |
| Tensor-factorized neural networks | TNNLS | 2018 |
| Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks | IJCAI | 2018 |
| Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition | CVPR | 2018 |
| Wide Compression: Tensor Ring Nets | CVPR | 2018 |
DCFNet: Deep Neural Network with Decomposed Convolutional Filters  | ICML | 2018 |
Self-supervised Knowledge Distillation Using Singular Value Decomposition  | ECCV | 2018 |
| Extreme Network Compression via Filter Group Approximation | ECCV | 2018 |
Network Decoupling: From Regular to Depthwise Separable Convolutions  | BMVC | 2018 |
| On Compressing Deep Models by Low Rank and Sparse Decomposition | CVPR | 2017 |
Coordinating Filters for Faster Deep Neural Networks  | ICCV | 2017 |
| Factorized Convolutional Neural Networks | ICCVW | 2017 |
Tensor Regression Networks with various Low-Rank Tensor Approximations  | Arxiv | 2017 |
| Accelerating Very Deep Convolutional Networks for Classification and Detection | TPAMI | 2016 |
Convolutional Neural Networks With Low-rank Regularization  | ICLR | 2016 |
| Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications | ICLR | 2016 |
| Towards Convolutional Neural Networks Compression via Global Error Reconstruction | IJCAI | 2016 |
| Accelerating Convolutional Neural Networks for Mobile Applications | MM | 2016 |
Ultimate tensorization: compressing convolutional and FC layers alike  | NIPSW | 2016 |
| Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition | ICLR | 2015 |
| Speeding up Convolutional Neural Networks with Low Rank Expansions | BMVC | 2014 |
| Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation | NIPS | 2014 |
Tensor Decomposition
