Fetching the paper…
Reading the bibliography…
Deep neural networks (NNs) encounter scalability limitations when confronted with a vast array of neurons, thereby constraining their achievable network depth.
Self-Supervised Visual Feature Learning with Deep Neural Networks: A Survey
Jing, L. & Tian, Y · 1902
Earlier work this paper cites.
Compressing deep neural networks by matrix product operators
Gao, Z. F. et al · 1904
Earlier work this paper cites.
Deep learning for hyperspectral image classification: An overview
Li, S. et al · 1910
Earlier work this paper cites.
Real time evolution with neural-network quantum states
Gutiérrez, I. L. & Mendl, C. B · 1912
Earlier work this paper cites.
Density matrix formulation for quantum renormalization groups
White, S. R · 1992
Earlier work this paper cites.
Density-matrix algorithms for quantum renormalization groups
White, S. R · 1993
Earlier work this paper cites.
Artificial neural network models for forecasting and decision making
Hill, T., Marquez, L., O’Connor, M. & Remus, W · 1994
Earlier work this paper cites.
Reward-dependent learning in neuronal networks for planning and decision making
Dehaene, S. & Changeux, J. P · 2000
Earlier work this paper cites.
Efficient classical simulation of slightly entangled quantum computations
Vidal, G · 2003
Earlier work this paper cites.
Efficient simulation of one-dimensional quantum many-body systems
Vidal, G · 2004
Earlier work this paper cites.
Machine learning for condensed matter physics
Bedolla, E., Padierna, L. C. & Castañeda-Priego, R · 2005
Earlier work this paper cites.
Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems
Verstraete, F., Murg, V. & Cirac, J · 2008
Earlier work this paper cites.
Infinite time-evolving block decimation algorithm beyond unitary evolution
Orus, R. & Vidal, G · 2008
Earlier work this paper cites.
A Model Compression Method with Matrix Product Operators for Speech Enhancement
Sun, X., Gao, Z. F., Lu, Z. Y., Li, J. & Yan, Y · 2010
Earlier work this paper cites.
Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks
Yu, D., Seltzer, M. L., Li, J., Huang, J. T. & Seide, F · 2013
Earlier work this paper cites.
A practical introduction to tensor networks: Matrix product states and projected entangled pair states
Orus, R. R · 2014
Earlier work this paper cites.
Tensorizing neural networks
Novikov, A., Podoprikhin, D., Osokin, A. & Vetrov, D. P · 2015
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems (2015)
Abadi, M. et al · 2015
Earlier work this paper cites.
Supervised learning with tensor networks
Stoudenmire, E. & Schwab, D. J · 2016
Earlier work this paper cites.
Application of deep learning in object detection
Zhou, X., Gong, W., Fu, W. & Du, F · 2017
Earlier work this paper cites.
Deep learning for biological image classification
Affonso, C., Rossi, A. L. D., Vieira, F. H. A. & de Carvalho, A. C. P. d. L. F · 2017
Cited alongside, same era.
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I. & Hinton, G. E · 2017
Cited alongside, same era.
DeepCluster: A General Clustering Framework Based on Deep Learning
Tian, K., Zhou, S. & Guan, J · 2017
Cited alongside, same era.
Machine learning phases of matter
Carrasquilla, J. & Melko, R. G · 2017
Cited alongside, same era.
Detection of Phase Transition via Convolutional Neural Networks
Tanaka, A. & Tomiya, A · 2017
Cited alongside, same era.
Machine learning Z2 quantum spin liquids with quasiparticle statistics
Zhang, Y., Melko, R. G. & Kim, E. A · 2017
Cited alongside, same era.
Anomaly detection with tensor networks
Wang, J., Roberts, C., Vidal, G. & Leichenauer, S · 2020
Later among the works it cites.
Tensor Regression Networks
Kossaifi, J. et al · 2020
Later among the works it cites.
Physics-informed machine learning
Karniadakis, G. E. et al · 2021
Later among the works it cites.
Expressivity of quantum neural networks
Wu, Y., Yao, J., Zhang, P. & Zhai, H · 2021
Later among the works it cites.
Tensor Methods in Computer Vision and Deep Learning
Panagakis, Y. et al · 2021
Later among the works it cites.
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
Liu, P. et al · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tensor Contraction Layers for Parsimonious Deep Nets
Kossaifi, J., Khanna, A., Lipton, Z., Furlanello, T. & Anandkumar, A · 2017
Cited alongside, same era.
Deep learning for object identification in ROS-based mobile robots
Chang, Y. H., Chung, P. L. & Lin, H. W · 2018
Cited alongside, same era.
TensorFlow for deep learning : from linear regression to reinforcement learning (O’Reilly Media, Inc., 2018)
Ramsundar, B. & Zadeh, R. B · 2018
Cited alongside, same era.
A Survey of Clustering with Deep Learning: From the Perspective of Network Architecture
Min, E. et al · 2018
Cited alongside, same era.
Clustering with deep learning: Taxonomy and new methods
Aljalbout, E., Golkov, V., Siddiqui, Y. & Cremers, D · 2018
Cited alongside, same era.
Learning relevant features of data with multi-scale tensor networks
Stoudenmire, E. M · 2018
Cited alongside, same era.
An end-to-end trainable hybrid classical-quantum classifier
Chen, S. Y.-C., Huang, C.-M., Hsing, C.-W. & Kao, Y.-J · 2021
Later among the works it cites.
Neural tensor contractions and the expressive power of deep neural quantum states
Sharir, O., Shashua, A. & Carleo, G · 2022
Closest in time.
Neural-network quantum states for periodic systems in continuous space
Pescia, G., Han, J., Lovato, A., Lu, J. & Carleo, G · 2022
Closest in time.
Neural-network quantum states for periodic systems in continuous space
Pescia, G., Han, J., Lovato, A., Lu, J. & Carleo, G · 2022
Closest in time.
Quantum-inspired tensor neural networks for partial differential equations
Patel, R. G. et al · 2022
Closest in time.
Strashko, A. & Stoudenmire, E. M · 2022
Closest in time.
Barratt, F., Dborin, J. & Wright, L · 2022
Closest in time.
Boosting defect detection in manufacturing using tensor convolutional neural networks
Martin-Ramiro, P. et al · 2022
Closest in time.
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
Qi, J., Yang, C.-H. H., Chen, P.-Y. & Tejedor, J · 2023
Closest in time.
Theoretical error performance analysis for variational quantum circuit based functional regression
Qi, J., Yang, C.-H. H., Chen, P.-Y. & Hsieh, M.-H · 2023
Closest in time.
Martin-Ramiro, P., de la Maza, U. S., Singh, S., Orus, R. & Mugel, S · 2024
Closest in time.
Tensor network compressibility of convolutional models, DOI: 10.48550/arXiv.2403.14379 (2024)
Singh, S., Jahromi, S. S. & Orus, R · 2024
Closest in time.
Tomut, A. et al · 2024
Closest in time.