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Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning.
Differentiable programming tensor networks
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Programming techniques: Regular expression search algorithm
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Ambiguity in graphs and expressions
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Finitely correlated states on quantum spin chains
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Density matrix formulation for quantum renormalization groups
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An EM approach to learning sequential behavior
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Predictive representations of state
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Quantum Computation and Quantum Information
Nielsen, M. A. and Chuang, I. L. (2002) · 2002
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Matrix product state representations
Perez-García, D., Verstraete, F., Wolf, M. M., and Cirac, J. I. (2007) · 2007
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On rational stochastic languages
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Handbook of weighted automata
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Mathematical foundations for a compositional distributional model of meaning
Coecke, B., Sadrzadeh, M., and Clark, S. (2010) · 2010
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Hidden quantum markov models and non-adaptive read-out of many-body states
Monras, A., Beige, A., and Wiesner, K. (2010) · 2010
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Quadratic weighted automata: Spectral algorithm and likelihood maximization
Bailly, R. (2011) · 2011
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Tensor-train decomposition
Oseledets, I. V. (2011) · 2011
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Perfect sampling with unitary tensor networks
Ferris, A. J. and Vidal, G. (2012) · 2012
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Adam: A method for stochastic optimization
Pestun, V. and Vlassopoulos, Y. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
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Exact holographic tensor networks for the Motzkin spin chain
Alexander, R. N., Evenbly, G., and Klich, I. (2018) · 2018
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JAX: composable transformations of Python+NumPy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., and Wanderman-Milne, S. (2018) · 2018
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Supervised learning with generalized tensor networks
Glasser, I., Pancotti, N., and Cirac, J. I. (2018) · 2018
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Kingma, D. P. and Ba, J. (2015) · 2015
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Tensorizing neural networks
Novikov, A., Podoprikhin, D., Osokin, A., and Vetrov, D. P. (2015) · 2015
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On the expressive power of deep learning: A tensor analysis
Cohen, N., Sharir, O., and Shashua, A. (2016) · 2016
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Supervised learning with tensor networks
Stoudenmire, E. and Schwab, D. J. (2016) · 2016
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Input switched affine networks: an rnn architecture designed for interpretability
Foerster, J. N., Gilmer, J., Sohl-Dickstein, J., Chorowski, J., and Sussillo, D. (2017) · 2017
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Language design as information renormalization
Gallego, A. and Orús, R. (2017) · 2017
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Convolutional sequence to sequence learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., and Dauphin, Y. N. (2017) · 2017
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Unsupervised generative modeling using matrix product states
Han, Z.-Y., Wang, J., Fan, H., Wang, L., and Zhang, P. (2018) · 2018
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Parallelizing linear recurrent neural nets over sequence length
Martin, E. and Cundy, C. (2018) · 2018
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Learning hidden quantum markov models
Srinivasan, S., Gordon, G., and Boots, B. (2018) · 2018
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Learning relevant features of data with multi-scale tensor networks
Stoudenmire, E. M. (2018) · 2018
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Singular value automata and approximate minimization
Balle, B., Panangaden, P., and Precup, D. (2019) · 2019
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Tree tensor networks for generative modeling
Cheng, S., Wang, L., Xiang, T., and Zhang, P. (2019) · 2019
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Random language model
DeGiuli, E. (2019) · 2019
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Tensor networks for complex quantum systems
Orús, R. (2019) · 2019
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Connecting weighted automata and recurrent neural networks through spectral learning
Rabusseau, G., Li, T., and Precup, D. (2019) · 2019
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Probabilistic modeling with matrix product states
Stokes, J. and Terilla, J. (2019) · 2019
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