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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN" "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
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<title>mlrelatedworks</title>
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<h2 style="text-indent: -27px; margin-left: 47px; margin-right: 20px" align="center">
<size="6"><b style>
<span style="font-family: Times New Romen; font-weight:400" lang="EN-US">List of
Related Works in between ML and TN</span></b></font></h2>
<div class="csl-bib-body" style="line-height: 1.35; ">
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">1. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Bény C. Deep learning and the renormalization group. arXiv:13013124 [quant-ph] . 2013 Jan 14; <a href="http://arxiv.org/abs/1301.3124">http://arxiv.org/abs/1301.3124</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep%20learning%20and%20the%20renormalization%20group&rft.jtitle=arXiv%3A1301.3124%20%5Bquant-ph%5D&rft.aufirst=C%C3%A9dric&rft.aulast=B%C3%A9ny&rft.au=C%C3%A9dric%20B%C3%A9ny&rft.date=2013-01-14"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">2. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Dumoulin V, Goodfellow IJ, Courville A, Bengio Y. On the Challenges of Physical Implementations of RBMs. arXiv:13125258 [cs, stat] . 2013 Dec 18 ; <a href="http://arxiv.org/abs/1312.5258">http://arxiv.org/abs/1312.5258</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=On%20the%20Challenges%20of%20Physical%20Implementations%20of%20RBMs&rft.jtitle=arXiv%3A1312.5258%20%5Bcs%2C%20stat%5D&rft.aufirst=Vincent&rft.aulast=Dumoulin&rft.au=Vincent%20Dumoulin&rft.au=Ian%20J.%20Goodfellow&rft.au=Aaron%20Courville&rft.au=Yoshua%20Bengio&rft.date=2013-12-18"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">3. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Mehta P, Schwab DJ. An exact mapping between the variational renormalization group and deep learning. arXiv preprint arXiv:14103831 . 2014 ; <a href="http://arxiv.org/abs/1410.3831">http://arxiv.org/abs/1410.3831</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An%20exact%20mapping%20between%20the%20variational%20renormalization%20group%20and%20deep%20learning&rft.jtitle=arXiv%20preprint%20arXiv%3A1410.3831&rft.aufirst=Pankaj&rft.aulast=Mehta&rft.au=Pankaj%20Mehta&rft.au=David%20J.%20Schwab&rft.date=2014"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">4. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Steeg GV, Galstyan A. Discovering Structure in High-Dimensional Data Through Correlation Explanation. arXiv:14061222 [cs, stat] . 2014 Jun 4 ; <a href="http://arxiv.org/abs/1406.1222">http://arxiv.org/abs/1406.1222</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Discovering%20Structure%20in%20High-Dimensional%20Data%20Through%20Correlation%20Explanation&rft.jtitle=arXiv%3A1406.1222%20%5Bcs%2C%20stat%5D&rft.aufirst=Greg%20Ver&rft.aulast=Steeg&rft.au=Greg%20Ver%20Steeg&rft.au=Aram%20Galstyan&rft.date=2014-06-04"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">5. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Cichocki A. Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems. arXiv:14073124 [cs, math] . 2014 Jul 11; <a href="http://arxiv.org/abs/1407.3124">http://arxiv.org/abs/1407.3124</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Tensor%20Networks%20for%20Big%20Data%20Analytics%20and%20Large-Scale%20Optimization%20Problems&rft.jtitle=arXiv%3A1407.3124%20%5Bcs%2C%20math%5D&rft.aufirst=Andrzej&rft.aulast=Cichocki&rft.au=Andrzej%20Cichocki&rft.date=2014-07-11"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">6. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Arsenault L-F, Lopez-Bezanilla A, von Lilienfeld OA, Millis AJ. Machine learning for many-body physics: The case of the Anderson impurity model. Physical Review B . 2014 Oct 31 ;90(15). <a href="http://link.aps.org/doi/10.1103/PhysRevB.90.155136">http://link.aps.org/doi/10.1103/PhysRevB.90.155136</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=info%3Adoi%2F10.1103%2FPhysRevB.90.155136&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine%20learning%20for%20many-body%20physics%3A%20The%20case%20of%20the%20Anderson%20impurity%20model&rft.jtitle=Physical%20Review%20B&rft.volume=90&rft.issue=15&rft.aufirst=Louis-Fran%C3%A7ois&rft.aulast=Arsenault&rft.au=Louis-Fran%C3%A7ois%20Arsenault&rft.au=Alejandro%20Lopez-Bezanilla&rft.au=O.%20Anatole%20von%20Lilienfeld&rft.au=Andrew%20J.%20Millis&rft.date=2014-10-31&rft.issn=1098-0121%2C%201550-235X&rft.language=en"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">7. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Tishby N, Zaslavsky N. Deep Learning and the Information Bottleneck Principle. arXiv:150302406 [cs] . 2015 Mar 9; <a href="http://arxiv.org/abs/1503.02406">http://arxiv.org/abs/1503.02406</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep%20Learning%20and%20the%20Information%20Bottleneck%20Principle&rft.jtitle=arXiv%3A1503.02406%20%5Bcs%5D&rft.aufirst=Naftali&rft.aulast=Tishby&rft.au=Naftali%20Tishby&rft.au=Noga%20Zaslavsky&rft.date=2015-03-09"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">8. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Cai X-D, Wu D, Su Z-E, Chen M-C, Wang X-L, Li L, et al. Entanglement-Based Machine Learning on a Quantum Computer. Physical Review Letters . 2015 Mar 19 ;114(11). <a href="http://link.aps.org/doi/10.1103/PhysRevLett.114.110504">http://link.aps.org/doi/10.1103/PhysRevLett.114.110504</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=info%3Adoi%2F10.1103%2FPhysRevLett.114.110504&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Entanglement-Based%20Machine%20Learning%20on%20a%20Quantum%20Computer&rft.jtitle=Physical%20Review%20Letters&rft.volume=114&rft.issue=11&rft.aufirst=X.-D.&rft.aulast=Cai&rft.au=X.-D.%20Cai&rft.au=D.%20Wu&rft.au=Z.-E.%20Su&rft.au=M.-C.%20Chen&rft.au=X.-L.%20Wang&rft.au=Li%20Li&rft.au=N.-L.%20Liu&rft.au=C.-Y.%20Lu&rft.au=J.-W.%20Pan&rft.date=2015-03-19&rft.issn=0031-9007%2C%201079-7114&rft.language=en"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">9. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Gabrié M, Tramel EW, Krzakala F. Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy. arXiv:150602914 [cond-mat, stat] . 2015 Jun 9 ; <a href="http://arxiv.org/abs/1506.02914">http://arxiv.org/abs/1506.02914</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Training%20Restricted%20Boltzmann%20Machines%20via%20the%20Thouless-Anderson-Palmer%20Free%20Energy&rft.jtitle=arXiv%3A1506.02914%20%5Bcond-mat%2C%20stat%5D&rft.aufirst=Marylou&rft.aulast=Gabri%C3%A9&rft.au=Marylou%20Gabri%C3%A9&rft.au=Eric%20W.%20Tramel&rft.au=Florent%20Krzakala&rft.date=2015-06-09"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">10. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Carrasquilla J, Melko RG. Machine learning phases of matter. arXiv preprint arXiv:160501735 . 2016 May ; <a href="http://arxiv.org/abs/1605.01735">http://arxiv.org/abs/1605.01735</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine%20learning%20phases%20of%20matter&rft.jtitle=arXiv%20preprint%20arXiv%3A1605.01735&rft.aufirst=Juan&rft.aulast=Carrasquilla&rft.au=Juan%20Carrasquilla&rft.au=Roger%20G.%20Melko&rft.date=2016-05"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">11. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Stoudenmire EM, Schwab DJ. Supervised Learning with Quantum-Inspired Tensor Networks. arXiv:160505775 [cond-mat, stat] . 2016 May 18 ; <a href="http://arxiv.org/abs/1605.05775">http://arxiv.org/abs/1605.05775</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Supervised%20Learning%20with%20Quantum-Inspired%20Tensor%20Networks&rft.jtitle=arXiv%3A1605.05775%20%5Bcond-mat%2C%20stat%5D&rft.aufirst=E.%20Miles&rft.aulast=Stoudenmire&rft.au=E.%20Miles%20Stoudenmire&rft.au=David%20J.%20Schwab&rft.date=2016-05-18"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">12. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Carleo G, Troyer M. Solving the quantum many-body problem with artificial neural networks. Science . 2016 Jun;355(6325):602–6. <a href="http://science.sciencemag.org/content/355/6325/602">http://science.sciencemag.org/content/355/6325/602</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=info%3Adoi%2F10.1126%2Fscience.aag2302&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Solving%20the%20quantum%20many-body%20problem%20with%20artificial%20neural%20networks&rft.jtitle=Science&rft.volume=355&rft.issue=6325&rft.aufirst=Giuseppe&rft.aulast=Carleo&rft.au=Giuseppe%20Carleo&rft.au=Matthias%20Troyer&rft.date=2016-06&rft.pages=602-606&rft.spage=602&rft.epage=606&rft.issn=0036-8075%2C%201095-9203&rft.language=en"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">13. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Wang L. Discovering Phase Transitions with Unsupervised Learning. arXiv:160600318 [cond-mat, stat] . 2016 Jun 1 ; <a href="http://arxiv.org/abs/1606.00318">http://arxiv.org/abs/1606.00318</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Discovering%20Phase%20Transitions%20with%20Unsupervised%20Learning&rft.jtitle=arXiv%3A1606.00318%20%5Bcond-mat%2C%20stat%5D&rft.aufirst=Lei&rft.aulast=Wang&rft.au=Lei%20Wang&rft.date=2016-06-01"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">14. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Lin HW, Tegmark M. Why does deep and cheap learning work so well? arXiv:160808225 [cond-mat, stat] . 2016 Aug 29 ; <a href="http://arxiv.org/abs/1608.08225">http://arxiv.org/abs/1608.08225</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Why%20does%20deep%20and%20cheap%20learning%20work%20so%20well%3F&rft.jtitle=arXiv%3A1608.08225%20%5Bcond-mat%2C%20stat%5D&rft.aufirst=Henry%20W.&rft.aulast=Lin&rft.au=Henry%20W.%20Lin&rft.au=Max%20Tegmark&rft.date=2016-08-29"></span>
<div class="csl-entry" style="clear: left; margin-bottom: 1em;">
<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">15. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Cichocki A, Lee N, Oseledets IV, Phan A-H, Zhao Q, Mandic D. Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1. Foundations and Trends® in Machine Learning . 2016 Sep;9(4–5):249–429. <a href="http://arxiv.org/abs/1609.00893">http://arxiv.org/abs/1609.00893</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=info%3Adoi%2F10.1561%2F2200000059&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Low-Rank%20Tensor%20Networks%20for%20Dimensionality%20Reduction%20and%20Large-Scale%20Optimization%20Problems%3A%20Perspectives%20and%20Challenges%20PART%201&rft.jtitle=Foundations%20and%20Trends%C2%AE%20in%20Machine%20Learning&rft.volume=9&rft.issue=4-5&rft.aufirst=A.&rft.aulast=Cichocki&rft.au=A.%20Cichocki&rft.au=N.%20Lee&rft.au=I.%20V.%20Oseledets&rft.au=A.-H.%20Phan&rft.au=Q.%20Zhao&rft.au=D.%20Mandic&rft.date=2016-09&rft.pages=249-429&rft.spage=249&rft.epage=429&rft.issn=1935-8237%2C%201935-8245"></span>
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<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">16. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Ch’ng K, Carrasquilla J, Melko RG, Khatami E. Machine Learning Phases of Strongly Correlated Fermions. arXiv:160902552 [cond-mat] . 2016 Sep 8 ; <a href="http://arxiv.org/abs/1609.02552">http://arxiv.org/abs/1609.02552</a></div>
</div>
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine%20Learning%20Phases%20of%20Strongly%20Correlated%20Fermions&rft.jtitle=arXiv%3A1609.02552%20%5Bcond-mat%5D&rft.aufirst=Kelvin&rft.aulast=Ch'ng&rft.au=Kelvin%20Ch'ng&rft.au=Juan%20Carrasquilla&rft.au=Roger%20G.%20Melko&rft.au=Ehsan%20Khatami&rft.date=2016-09-08"></span>
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<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 2em;">17. </div><div class="csl-right-inline" style="margin: 0 .4em 0 2.5em;">Nieuwenburg V, L EP, Liu Y-H, Huber SD. Learning phase transitions by confusion. 2016 Oct 6 ; <a href="https://arxiv.org/abs/1610.02048">https://arxiv.org/abs/1610.02048</a></div>
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