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Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling approaches.
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J. Qi, C.-H. H. Yang, P.-Y. Chen, and J. Tejedor, “Exploiting low-rank tensor-train deep neural networks based on riemannian gradient descent with illustrations of speech processing,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 31, pp. 633–642, 2023
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2023
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S. Jie and Z.-H. Deng, “Fact: Factor-tuning for lightweight adaptation on vision transformer,” in Proceedings of the AAAI conference on artificial intelligence , vol. 37, no. 1, 2023, pp. 1060–1068
2023
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Z. Chen, L. Newhouse, E. Chen, D. Luo, and M. Soljacic, “Antn: Bridging autoregressive neural networks and tensor networks for quantum many-body simulation,” Advances in Neural Information Processing Systems , vol. 36, pp. 450–476, 2023
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2023
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Y. Pan, Y. Yuan, Y. Yin, Z. Xu, L. Shang, X. Jiang, and Q. Liu, “Reusing pretrained models by multi-linear operators for efficient training,” Advances in Neural Information Processing Systems , vol. 36, pp. 3248–3262, 2023
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Y. Gong, M. Yin, L. Huang, J. Xiao, Y. Sui, C. Deng, and B. Yuan, “Ette: Efficient tensor-train-based computing engine for deep neural networks,” in Proceedings of the 50th Annual International Symposium on Computer Architecture , 2023, pp. 1–13
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OpenAI, “GPT-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
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2023
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P. Parhami, M. Fateh, M. Rezvani, and H. Alinejad-Rokny, “A comparison of deep neural network models for cluster cancer patients through somatic point mutations,” Journal of Ambient Intelligence and Humanized Computing , vol. 14, no. 8, pp. 10 883–10 898, 2023
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2023
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N. Jouppi, G. Kurian, S. Li, P. Ma, R. Nagarajan, L. Nai, N. Patil, S. Subramanian, A. Swing, B. Towles et al. , “Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings,” in Proceedings of the 50th Annual International Symposium on Computer Architecture , 2023, pp. 1–14
2023
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I. Belyaeva, B. Gabrielson, Y.-P. Wang, T. W. Wilson, V. D. Calhoun, J. M. Stephen, and T. Adali, “Learning spatiotemporal brain dynamics in adolescents via multimodal meg and fmri data fusion using joint tensor/matrix decomposition,” IEEE Transactions on Biomedical Engineering , 2024
2024
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Y. Liu, J. Li, J. L. Wisnowski, and R. M. Leahy, “Graph learning for cortical parcellation from tensor decompositions of resting-state fmri,” bioRxiv , 2024
2024
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T. Kwon, J. Ko, J. Jung, J.-G. Jang, and K. Shin, “Compact decomposition of irregular tensors for data compression: From sparse to dense to high-order tensors,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 1451–1462
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2024
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H. Liu, M. Liu, J. Wang, X. Xie, and L. Yang, “Non-intrusive speech quality assessment with multi-task learning based on tensor network,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 851–855
2024
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R. Wang, J. Zhu, S. Wang, T. Wang, J. Huang, and X. Zhu, “Multi-modal emotion recognition using tensor decomposition fusion and self-supervised multi-tasking,” International Journal of Multimedia Information Retrieval , vol. 13, no. 4, p. 39, 2024
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K. Xie, C. Liu, X. Wang, X. Li, G. Xie, J. Wen, and K. Li, “Neural network compression based on tensor ring decomposition,” IEEE Transactions on Neural Networks and Learning Systems , 2024
2024
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M. Wang, Y. Zhen, Y. Pan, Y. Zhao, C. Zhuang, Z. Xu, R. Guo, and X. Zhao, “Tensorized hypergraph neural networks,” in Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) . SIAM, 2024, pp. 127–135
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2024
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V. Abronin, A. Naumov, D. Mazur, D. Bystrov, K. Tsarova, A. Melnikov, S. Dolgov, R. Brasher, and M. Perelshein, “Tqcompressor: improving tensor decomposition methods in neural networks via permutations,” in 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR) . IEEE, 2024, pp. 503–506
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2024
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X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino, “Superlora: Parameter-efficient unified adaptation for large vision models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8050–8055
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2024
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Anthropic, “Model card: Claude 3,” Anthropic, Tech. Rep., 2024. [Online]. Available: https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model_Card_Claude_3.pdf
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2024
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2024
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A. S. Dhanjal and W. Singh, “A comprehensive survey on automatic speech recognition using neural networks,” Multimedia Tools and Applications , vol. 83, no. 8, pp. 23 367–23 412, 2024
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G. Ahdritz, N. Bouatta, S. Kadyan, L. Jarosch, D. Berenberg, I. Fisk, A. Watkins, S. Ra, R. Bonneau, and M. AlQuraishi, “Openproteinset: Training data for structural biology at scale,” Advances in Neural Information Processing Systems , vol. 36, 2024
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Y. Qiu, G. Zhou, C. Li, D. Mandic, and Q. Zhao, “Tensor ring rank determination using odd-dimensional unfolding,” Neural Networks , p. 106947, 2024
2024
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M. Li, R. B. Basat, S. Vargaftik, C. Lao, K. Xu, M. Mitzenmacher, and M. Yu, “ { \{ THC } \} : Accelerating distributed deep learning using tensor homomorphic compression,” in 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24) , 2024, pp. 1191–1211
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Y. Zhang, Y. Liu, H. Yuan, Z. Qin, Y. Yuan, Q. Gu, and A. C.-C. Yao, “Tensor product attention is all you need,” 2025
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2025
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X. Chen, Z. Li, L. He, and X. Liu, “Tensor4ml: Tensor decomposition for machine learning,” https://github.com/xinychen/Tensor4ML , 2024, accessed: 2025-01-12
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