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States of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modelled.
1906
Earlier work this paper cites.
D. Harrison Jr and D. L. Rubinfeld, “Hedonic housing prices and the demand for clean air,” J. Environ. Econ. Manage. , vol. 5, no. 1, pp. 81–102, 1978
1978
Earlier work this paper cites.
S. Boyd and L. Chua, “Fading memory and the problem of approximating nonlinear operators with volterra series,” IEEE Trans. Circuits Syst. , vol. 32, no. 11, pp. 1150–1161, 1985
1985
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Comput. , vol. 1, no. 4, pp. 541–551, 1989
1989
Earlier work this paper cites.
J. B. Tenenbaum and W. T. Freeman, “Separating style and content with bilinear models,” Neural Comput. , vol. 12, no. 6, pp. 1247–1283, 2000
2000
Earlier work this paper cites.
A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput. , vol. 14, no. 3, pp. 199–222, 2004
2004
Earlier work this paper cites.
S. Weisberg, Applied linear regression . John Wiley & Sons, 2005
2005
Earlier work this paper cites.
Y. Dodge, D. Cox, and D. Commenges, The Oxford dictionary of statistical terms . Oxford University Press on Demand, 2006
2006
Earlier work this paper cites.
H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “Mpca: Multilinear principal component analysis of tensor objects,” IEEE Trans. Neural Networks , vol. 19, no. 1, pp. 18–39, 2008
2008
Earlier work this paper cites.
D. A. Freedman, Statistical models: theory and practice . Cambridge University Press, 2009
2009
Earlier work this paper cites.
Y. LeCun and C. Cortes. (2010) MNIST handwritten digit database. [Online]. Available: http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
——, “A survey of multilinear subspace learning for tensor data,” Pattern Recognit. , vol. 44, no. 7, pp. 1540–1551, 2011
2011
Earlier work this paper cites.
G. Evenbly and G. Vidal, “Tensor network states and geometry,” J. Stat. Phys. , vol. 145, no. 4, pp. 891–918, 2011
2011
Earlier work this paper cites.
A. Sordoni, J.-Y. Nie, and Y. Bengio, “Modeling term dependencies with quantum language models for ir,” in Proc. 36th Int. ACM SIGIR Conf. Res. Develop. Inf. Retr. , Jul. 2013, pp. 653–662
2013
Earlier work this paper cites.
B. L. Karihaloo, A. R. Murthy, and N. R. Iyer, “Determination of size-independent specific fracture energy of concrete mixes by the tri-linear model,” Cem. Concr. Res. , vol. 49, pp. 82–88, 2013
2013
Earlier work this paper cites.
R. Socher, D. Chen, C. D. Manning, and A. Ng, “Reasoning with neural tensor networks for knowledge base completion,” in Proc. NIPS , Dec. 2013, pp. 926–934
2013
Earlier work this paper cites.
V. Vapnik, The Nature of Statistical Learning Theory . Springer science & business media, 2013
2013
Earlier work this paper cites.
A. M. Saxe, J. L. McClelland, and S. Ganguli, “Exact solutions to the nonlinear dynamics of learning in deep linear neural networks,” in Proc. ICLR , May 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear cnn models for fine-grained visual recognition,” in Proc. IEEE conf. CVPR , Jun. 2015, pp. 1449–1457
2015
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Cited alongside, same era.
X. Qiu and X. Huang, “Convolutional neural tensor network architecture for community-based question answering,” in Conf. IJCAI , Jul. 2015
2015
G. Hu, X. Peng, Y. Yang, T. M. Hospedales, and J. Verbeek, “Frankenstein: Learning deep face representations using small data,” IEEE Trans. Image Process. , vol. 27, no. 1, pp. 293–303, 2017
2017
Later among the works it cites.
M. Luo, X. Chang, L. Nie, Y. Yang, A. G. Hauptmann, and Q. Zheng, “An adaptive semisupervised feature analysis for video semantic recognition,” IEEE Trans. Cybern. , vol. 48, no. 2, pp. 648–660, 2017
2017
Later among the works it cites.
S. S. Schoenholz, J. Gilmer, S. Ganguli, and J. Sohl-Dickstein, “Deep information propagation,” in Proc. ICLR , Apr. 2017
2017
Later among the works it cites.
Z.-Y. Han, J. Wang, H. Fan, L. Wang, and P. Zhang, “Unsupervised generative modeling using matrix product states,” Phys. Rev. X , vol. 8, no. 3, p. 031012, 2018
2018
Later among the works it cites.
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alphaXiv is searching for related work…
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. ICML , Jul. 2015, pp. 448–456
2015
Cited alongside, same era.
Y. Gao, O. Beijbom, N. Zhang, and T. Darrell, “Compact bilinear pooling,” in Proc. IEEE conf. CVPR , Jun. 2016, pp. 317–326
2016
Cited alongside, same era.
E. Stoudenmire and D. J. Schwab, “Supervised learning with tensor networks,” in Proc. NIPS , Dec. 2016, pp. 4799–4807
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE CVPR , Jun. 2016, pp. 770–778
2016
Cited alongside, same era.
K. Lin, J. Xu, I. M. Baytas, S. Ji, and J. Zhou, “Multi-task feature interaction learning,” in Proc. ACM SIGKDD , Aug. 2016, pp. 1735–1744
2016
Cited alongside, same era.
M. Blondel, M. Ishihata, A. Fujino, and N. Ueda, “Polynomial networks and factorization machines: new insights and efficient training algorithms,” in Proc. ICML , Jun. 2016, pp. 850–858
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Proc. ECCV , Oct. 2016, pp. 630–645
2016
Cited alongside, same era.
D. Liu, S.-J. Ran, P. Wittek, C. Peng, R. B. García, G. Su, and M. Lewenstein, “Machine learning by two-dimensional hierarchical tensor networks: A quantum information theoretic perspective on deep architectures,” in Proc. ICLR , May 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
H. Zheng, J. Fu, Z.-J. Zha, and J. Luo, “Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition,” in Proc. IEEE conf. CVPR , Jun. 2019, pp. 5012–5021
2019
Later among the works it cites.
T. Do, T.-T. Do, H. Tran, E. Tjiputra, and Q. D. Tran, “Compact trilinear interaction for visual question answering,” in Proc. ICCV , Nov. 2019, pp. 392–401
2019
Later among the works it cites.
W. Huggins, P. Patil, B. Mitchell, K. B. Whaley, and E. M. Stoudenmire, “Towards quantum machine learning with tensor networks,” Quantum Sci. Technol. , vol. 4, no. 2, p. 024001, 2019
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Proc. NIPS , Dec. 2019, pp. 8026–8037
2019
Later among the works it cites.
D. Zhang, L. Yao, K. Chen, S. Wang, X. Chang, and Y. Liu, “Making sense of spatio-temporal preserving representations for eeg-based human intention recognition,” IEEE Trans. Cybern. , vol. 50, no. 7, pp. 3033–3044, 2019
2019
Later among the works it cites.
K. Chen, L. Yao, D. Zhang, X. Wang, X. Chang, and F. Nie, “A semisupervised recurrent convolutional attention model for human activity recognition,” IEEE Trans. Neural Networks Learn. Syst. , vol. 31, no. 5, pp. 1747–1756, 2019
2019
Later among the works it cites.
Y. Xu, Z. Wu, J. Chanussot, and Z. Wei, “Hyperspectral images super-resolution via learning high-order coupled tensor ring representation,” IEEE Trans. Neural Networks Learn. Syst. , vol. 31, no. 11, pp. 4747–4760, 2020
2020
Later among the works it cites.
Y. Liu, J. Liu, and C. Zhu, “Low-rank tensor train coefficient array estimation for tensor-on-tensor regression,” IEEE Trans. Neural Networks Learn. Syst. , vol. 31, no. 12, pp. 5402–5411, 2020
2020
Later among the works it cites.
A. Singh, V. Kotiyal, S. Sharma, J. Nagar, and C.-C. Lee, “A machine learning approach to predict the average localization error with applications to wireless sensor networks,” IEEE Access , vol. 8, pp. 208 253–208 263, 2020
2020
Later among the works it cites.
2021
Closest in time.
Y. Chen, Y. Pan, and D. Dong, “Quantum language model with entanglement embedding for question answering,” IEEE Trans. Cybern. , 2021. [Online]. Available: https://doi.org/10.1109/tcyb.2021.3131252
2021
Closest in time.