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Hypercomplex neural networks have proven to reduce the overall number of parameters while ensuring valuable performance by leveraging the properties of Clifford algebras.
H. V. Henderson, F. Pukelsheim, and S. R. Searle, “On the history of the kronecker product,” Linear and Multilinear Algebra , vol. 14, no. 2, pp. 113–120, 1983
1983
Earlier work this paper cites.
T. Nitta, “A quaternary version of the back-propagation algorithm,” 1995, pp. 2753–2756
1995
Earlier work this paper cites.
A. Hirose, I. Aizenberg, and D. P. Mandic, “Guest editorial special issue on complex- and hypercomplex-valued neural networks,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 25, no. 9, pp. 1597–1599, 2014
2014
Earlier work this paper cites.
T. K. Paul and T. Ogunfunmi, “A kernel adaptive algorithm for quaternion-valued inputs,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 26, no. 10, pp. 2422–2439, Oct. 2015
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Int. Conf. on Learning Representations (ICLR) , San Diego, CA, USA, 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold, M. Slaney, R. J. Weiss, and K. Wilson, “CNN architectures for large-scale audio classification,” in IEEE Int. Conf. on Acoustics, Speech and Signal Process. (ICASSP) , 2017, pp. 131–135
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in IEEE Int. Conf. on Computer Vision (ICCV) , 2017, pp. 618–626
2017
Earlier work this paper cites.
S. Sanei, C. C. Took, and S. Enshaeifar, “Quaternion adaptive line enhancer based on singular spectrum analysis,” in IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP) , 2018, pp. 2876–2880
2018
Earlier work this paper cites.
M. Xiang, S. Enshaeifar, A. E. Stott, C. C. Took, Y. Xia, S. Kanna, and D. P. Mandic, “Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing,” Signal Process. , vol. 148, pp. 193–204, 2018
2018
Earlier work this paper cites.
M. E. Valle and F. Z. De Castro, “On the dynamics of hopfield neural networks on unit quaternions,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 29, no. 6, pp. 2464–2471, 2018
2018
Earlier work this paper cites.
Y. Liu, D. Zhang, J. Lou, J. Lu, and J. Cao, “Stability analysis of quaternion-valued neural networks: Decomposition and direct approaches,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 29, no. 9, pp. 4201–4211, 2018
2018
Earlier work this paper cites.
C. Gaudet and A. Maida, “Deep quaternion networks,” in IEEE Int. Joint Conf. on Neural Netw. (IJCNN) , Rio de Janeiro, Brazil, Jul. 2018
2018
Earlier work this paper cites.
E. Real, A. Aggarwal, Y. Huang, and Q. Le, “Regularized evolution for image classifier architecture search,” Proceedings of the AAAI Conf. on Artificial Intelligence , vol. 33, pp. 4780–4789, Jul. 2019
2019
Earlier work this paper cites.
T. Parcollet, M. Ravanelli, M. Morchid, G. Linarès, C. Trabelsi, R. De Mori, and Y. Bengio, “Quaternion recurrent neural networks,” in Int. Conf. on Learning Representations (ICLR) , New Orleans, LA, May 2019, pp. 1–19
2019
Earlier work this paper cites.
Y. Tay, A. Zhang, A. T. Luu, J. Rao, S. Zhang, S. Wang, J. Fu, and C. S. Hui, “Lightweight and efficient neural natural language processing with quaternion networks,” in ACL (1) . Association for Computational Linguistics, 2019, pp. 1494–1503
2019
Earlier work this paper cites.
C. C. Took and Y. Xia, “Multichannel quaternion least mean square algorithm,” in IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP) , 2019, pp. 8524–8527
2019
Earlier work this paper cites.
T. Parcollet, M. Morchid, and G. Linarès, “A survey of quaternion neural networks,” Artif. Intell. Rev. , Aug. 2019
2019
Earlier work this paper cites.
T. Parcollet, M. Morchid, and G. Linarès, “Quaternion convolutional neural networks for heterogeneous image processing,” in IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP) , Brighton, UK, May 2019, pp. 8514–8518
2019
Earlier work this paper cites.
D. Comminiello, M. Lella, S. Scardapane, and A. Uncini, “Quaternion convolutional neural networks for detection and localization of 3D sound events,” in IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP) , Brighton, UK, May 2019, pp. 8533–8537
2019
Cited alongside, same era.
S. Adavanne, A. Politis, J. Nikunen, and T. Virtanen, “Sound event localization and detection of overlapping sources using convolutional recurrent neural networks,” IEEE Journal of Selected Topics in Signal Processing , vol. 13, pp. 34–48, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of StyleGAN,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
A. Cariow and G. Cariowa, “Fast algorithms for deep octonion networks,” IEEE Trans. Neural Netw. Learn. Syst. , Nov. 2021
2021
Closest in time.
M. E. Valle and R. A. Lobo, “Hypercomplex-valued recurrent correlation neural networks,” Neurocomputing , vol. 432, pp. 111–123, 2021
2021
Closest in time.
T. Chen, H. Yin, X. Zhang, Z. Huang, Y. Wang, and M. Wang, “Quaternion factorization machines: A lightweight solution to intricate feature interaction modeling,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1–14, 2021
2021
Closest in time.
E. Grassucci, D. Comminiello, and A. Uncini, “A quaternion-valued variational autoencoder,” in IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP) , Toronto, Canada, Jun. 2021
2021
Closest in time.
——, “An information-theoretic perspective on proper quaternion variational autoencoders,” Entropy , vol. 23, no. 7, 2021
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Cited alongside, same era.
J. Navarro-Moreno, R. M. Fernández-Alcalá, J. D. Jiménez-López, and J. C. Ruiz-Molina, “Tessarine signal processing under the t-properness condition,” Journal of the Franklin Institute , vol. 357, no. 14, pp. 10 100–10 126, 2020
2020
Cited alongside, same era.
M. E. Valle and R. A. Lobo, “Quaternion-valued recurrent projection neural networks on unit quaternions,” Theoretical Computer Science , vol. 843, pp. 136–152, 2020
2020
Cited alongside, same era.
F. Z. De Castro and M. E. Valle, “A broad class of discrete-time hypercomplex-valued Hopfield neural networks,” Neural Networks , vol. 122, pp. 54–67, 2020
2020
Cited alongside, same era.
J. Wu, L. Xu, F. Wu, Y. Kong, L. Senhadji, and H. Shu, “Deep octonion networks,” Neurocomputing , vol. 397, pp. 179–191, 2020
2020
Cited alongside, same era.
G. Vieira and M. E. Valle, “Extreme learning machines on Cayley-Dickson algebra applied for color image auto-encoding,” in IEEE Int. Joint Conf. on Neural Netw. (IJCNN) , 2020, pp. 1–8
2020
Cited alongside, same era.
2020
Cited alongside, same era.
C. Huang, A. Touati, P. Vincent, G. K. Dziugaite, A. Lacoste, and A. C. Courville, “Stochastic neural network with Kronecker flow,” in AISTATS , 2020
2020
Cited alongside, same era.
M. Ricciardi Celsi, S. Scardapane, and D. Comminiello, “Quaternion neural networks for 3D sound source localization in reverberant environments,” in IEEE Int. Workshop on Machine Learning for Signal Process. , Espoo, Finland, Sep. 2020, pp. 1–6
2020
Cited alongside, same era.
2021
Closest in time.
S. Gai and X. Huang, “Reduced biquaternion convolutional neural network for color image processing,” IEEE Trans. on Circuits and Systems for Video Technology , pp. 1–1, 2021
2021
Closest in time.
C. J. Gaudet and A. S. Maida, “Removing dimensional restrictions on complex/hyper-complex neural networks,” in 2021 IEEE Int. Conf. on Image Process. (ICIP) , 2021, pp. 319–323
2021
Closest in time.
A. Cariow and G. Cariowa, “Fast algorithms for quaternion-valued convolutional neural networks,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 32, no. 1, pp. 457–462, 2021
2021
Closest in time.
Z. Tang, F. Jiang, M. Gong, H. Li, Y. Wu, F. Yu, Z. Wang, and M. Wang, “SKFAC: Training neural networks with faster Kronecker-factored approximate curvature,” in IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 13 479–13 487
2021
Closest in time.
D. Wang, B. Wu, G. S. Zhao, H. Chen, L. Deng, T. Yan, and G. Li, “Kronecker CP decomposition with fast multiplication for compressing RNNs,” IEEE Trans. Neural Netw. Learn. Syst. , vol. PP, 2021
2021
Closest in time.
A. Zhang, Y. Tay, S. Zhang, A. Chan, A. T. Luu, S. C. Hui, and J. Fu, “Beyond fully-connected layers with quaternions: Parameterization of hypercomplex multiplications with 1 / n 1/n parameters,” Int. Conf. on Machine Learning (ICML) , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
A. Mesaros, T. Heittola, T. Virtanen, and M. D. Plumbley, “Sound event detection: A tutorial,” IEEE Signal Processing Magazine , vol. 38, no. 5, pp. 67–83, 2021
2021
Closest in time.
E. Guizzo, R. F. Gramaccioni, S. Jamili, C. Marinoni, E. Massaro, C. Medaglia, G. Nachira, L. Nucciarelli, L. Paglialunga, M. Pennese, S. Pepe, E. Rocchi, A. Uncini, and D. Comminiello, “L3DAS21 Challenge: Machine learning for 3D audio signal processing,” 2021 IEEE Int. Workshop on Machine Learning for Signal Process. (MLSP) , 2021
2021
Closest in time.
E. Grassucci, E. Cicero, and D. Comminiello, “Quaternion generative adversarial networks,” in Generative Adversarial Learning: Architectures and Applications , R. Razavi-Far, A. Ruiz-Garcia, V. Palade, and J. Schmidhuber, Eds. Cham: Springer International Publishing, 2022, pp. 57–86
2022
Closest in time.