Machine learning for condensed matter physics
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Luis Carlos Padierna | Edwin A. Bedolla-Montiel | Ram'on Castaneda-Priego | R. Castañeda-Priego | L. C. Padierna | Edwin Bedolla
[1] Stefan Wessel,et al. Parameter diagnostics of phases and phase transition learning by neural networks , 2018, Physical Review B.
[2] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[3] Miao‐kun Sun,et al. Trends in cognitive sciences , 2012 .
[4] G. Jackson,et al. The liquid-crystalline phase behaviour of hard spherocylinders with terminal point dipoles , 1996 .
[5] Yuki Nagai,et al. Self-learning Monte Carlo method with Behler-Parrinello neural networks , 2018, Physical Review B.
[6] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[7] Simona Cocco,et al. Learning protein constitutive motifs from sequence data , 2018, eLife.
[8] Haruki Nakamura,et al. The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data , 2006, Nucleic Acids Res..
[9] R. Zecchina,et al. Inverse statistical problems: from the inverse Ising problem to data science , 2017, 1702.01522.
[10] Brian L. DeCost,et al. Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach , 2017 .
[11] J. Ashkin,et al. Two Problems in the Statistical Mechanics of Crystals. I. The Propagation of Order in Crystal Lattices. I. The Statistics of Two-Dimensional Lattices with Four Components. , 1943 .
[12] N. Wagner,et al. Dynamical arrest, percolation, gelation, and glass formation in model nanoparticle dispersions with thermoreversible adhesive interactions. , 2012, Langmuir : the ACS journal of surfaces and colloids.
[13] Yang Qi,et al. Self-learning Monte Carlo method , 2016, 1610.03137.
[14] Alfredo Vellido,et al. Neural networks in business: a survey of applications (1992–1998) , 1999 .
[15] Michael Walters,et al. Machine learning topological defects of confined liquid crystals in two dimensions. , 2019, Physical review. E.
[16] Hong-Ye Hu,et al. Machine learning holographic mapping by neural network renormalization group , 2019, Physical Review Research.
[17] Ivor W. Tsang,et al. Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..
[18] S. Chandrasekhar. Liquid Crystals: Cholesteric liquid crystals , 1992 .
[19] B. Alder,et al. Phase Transition for a Hard Sphere System , 1957 .
[20] K. Binder,et al. A Guide to Monte Carlo Simulations in Statistical Physics , 2000 .
[21] S. Manzhos,et al. Machine learning for the solution of the Schrödinger equation , 2020, Mach. Learn. Sci. Technol..
[22] Tomi Ohtsuki,et al. Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions , 2019 .
[23] Wayne A Hendrickson,et al. What is 'current opinion' in structural biology? , 2011, Current opinion in structural biology.
[24] William G. Hoover,et al. Melting Transition and Communal Entropy for Hard Spheres , 1968 .
[25] L. Onsager. Crystal statistics. I. A two-dimensional model with an order-disorder transition , 1944 .
[26] Taghi M. Khoshgoftaar,et al. A survey of transfer learning , 2016, Journal of Big Data.
[27] Shulin Wang,et al. Feature selection in machine learning: A new perspective , 2018, Neurocomputing.
[28] Érica R. Filletti,et al. Artificial neural networks for density-functional optimizations in fermionic systems , 2018, Scientific Reports.
[29] Vedika Khemani,et al. Machine Learning Out-of-Equilibrium Phases of Matter. , 2017, Physical review letters.
[30] Daria B Kokh,et al. Modeling and simulation of protein–surface interactions: achievements and challenges , 2016, Quarterly Reviews of Biophysics.
[31] Melissa C. Smith,et al. A generalized deep learning approach for local structure identification in molecular simulations , 2019, Chemical science.
[32] Xin Xu,et al. Kernel-Based Least Squares Policy Iteration for Reinforcement Learning , 2007, IEEE Transactions on Neural Networks.
[33] Klaus-Robert Müller,et al. Finding Density Functionals with Machine Learning , 2011, Physical review letters.
[34] Andrés Santos,et al. Note: equation of state and the freezing point in the hard-sphere model. , 2014, The Journal of chemical physics.
[35] Li Huang,et al. Accelerated Monte Carlo simulations with restricted Boltzmann machines , 2016, 1610.02746.
[36] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[37] J. Dobnikar,et al. Strain-induced domain formation in two-dimensional colloidal systems , 2006 .
[38] Risi Kondor,et al. Publisher’s Note: On representing chemical environments [Phys. Rev. B 87 , 184115 (2013)] , 2013 .
[39] David J. Schwab,et al. An exact mapping between the Variational Renormalization Group and Deep Learning , 2014, ArXiv.
[40] R. Melko,et al. TOPICAL REVIEW: Monte Carlo studies of the dipolar spin ice model , 2004 .
[41] Gülgün Kayakutlu,et al. Definition of artificial neural networks with comparison to other networks , 2011, WCIT.
[42] Erik Cambria,et al. Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..
[43] E. Ising. Beitrag zur Theorie des Ferromagnetismus , 1925 .
[44] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[45] Troels Arnfred Bojesen,et al. Policy-guided Monte Carlo: Reinforcement-learning Markov chain dynamics , 2018, Physical Review E.
[46] Roman M. Balabin,et al. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? , 2011, Physical chemistry chemical physics : PCCP.
[47] Vijayan K. Asari,et al. The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches , 2018, ArXiv.
[48] K-R Müller,et al. SchNet - A deep learning architecture for molecules and materials. , 2017, The Journal of chemical physics.
[49] K. Wilson. Renormalization Group and Critical Phenomena. I. Renormalization Group and the Kadanoff Scaling Picture , 1971 .
[50] J. Banavar,et al. Computer Simulation of Liquids , 1988 .
[51] J. Behler. Perspective: Machine learning potentials for atomistic simulations. , 2016, The Journal of chemical physics.
[52] Giancarlo Fissore,et al. Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics , 2018, Journal of Statistical Physics.
[53] Pradeep Kumar. The theory of critical phenomena: An introduction to the renormalization group. By J. J. Binney, N. J. Dowrick, A. J. Fisher, and M. E. J. Newman, Clarendon Press, Oxford, 1992. 464 pp. , 1993 .
[54] Roger G. Melko,et al. Machine learning phases of matter , 2016, Nature Physics.
[55] Jörg Behler,et al. Neural network molecular dynamics simulations of solid-liquid interfaces: water at low-index copper surfaces. , 2016, Physical chemistry chemical physics : PCCP.
[56] N. D. Mermin,et al. The topological theory of defects in ordered media , 1979 .
[57] Timothy J. Sluckin,et al. Crystals that flow: classic papers from the history of liquid crystals , 2004 .
[58] Luís Torgo,et al. OpenML: networked science in machine learning , 2014, SKDD.
[59] Liang Fu,et al. Self-learning Monte Carlo with deep neural networks , 2018, Physical Review B.
[60] Takamichi Terao,et al. A machine learning approach to analyze the structural formation of soft matter via image recognition , 2020, Soft Materials.
[61] Felix A Faber,et al. Crystal structure representations for machine learning models of formation energies , 2015, 1503.07406.
[62] Jasper Snoek,et al. Nonparametric guidance of autoencoder representations using label information , 2012, J. Mach. Learn. Res..
[63] Chris Yakopcic,et al. A State-of-the-Art Survey on Deep Learning Theory and Architectures , 2019, Electronics.
[64] Hava T. Siegelmann,et al. Support Vector Clustering , 2002, J. Mach. Learn. Res..
[65] Zohar Ringel,et al. Optimal Renormalization Group Transformation from Information Theory , 2018, Physical Review X.
[66] Michael Griebel,et al. A representer theorem for deep kernel learning , 2019, J. Mach. Learn. Res..
[67] Stephen Wu,et al. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm , 2019, npj Computational Materials.
[68] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[69] Emanuele Boattini,et al. Unsupervised learning for local structure detection in colloidal systems. , 2019, The Journal of chemical physics.
[70] H. Lekkerkerker,et al. Insights into phase transition kinetics from colloid science , 2002, Nature.
[71] Charu C. Aggarwal,et al. Linear Algebra and Optimization for Machine Learning: A Textbook , 2020 .
[72] J. Reif,et al. DNA-Templated Self-Assembly of Protein Arrays and Highly Conductive Nanowires , 2003, Science.
[73] F. Armstrong,et al. Current opinion in chemical biology. , 2012, Current opinion in chemical biology.
[74] Pramod P. Khargonekar,et al. Fast SVM training using approximate extreme points , 2013, J. Mach. Learn. Res..
[75] C. Giannetti,et al. Machine Learning as a universal tool for quantitative investigations of phase transitions , 2018, Nuclear Physics B.
[76] R. French. Catastrophic forgetting in connectionist networks , 1999, Trends in Cognitive Sciences.
[77] Kristof T. Schütt,et al. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties , 2013, 1307.1266.
[78] Wolfram Koch,et al. A Chemist's Guide to Density Functional Theory , 2000 .
[79] M. Marques,et al. Recent advances and applications of machine learning in solid-state materials science , 2019, npj Computational Materials.
[80] Giancarlo Fissore,et al. Spectral dynamics of learning in restricted Boltzmann machines , 2017 .
[81] Chuang,et al. Coarsening dynamics in uniaxial nematic liquid crystals. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[82] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[83] James L. McClelland,et al. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. , 1995, Psychological review.
[84] Thomas Gärtner,et al. Graph kernels and Gaussian processes for relational reinforcement learning , 2006, Machine Learning.
[85] Yi Zhang,et al. Machine learning Z 2 quantum spin liquids with quasiparticle statistics , 2017, 1705.01947.
[86] Björn Ommer,et al. Deep unsupervised learning of visual similarities , 2018, Pattern Recognit..
[87] Senlin Luo,et al. Deep supervised learning with mixture of neural networks , 2020, Artif. Intell. Medicine.
[88] Christoph Dellago,et al. Neural networks for local structure detection in polymorphic systems. , 2013, The Journal of chemical physics.
[89] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..
[90] E. Lindahl,et al. Membrane proteins: molecular dynamics simulations. , 2008, Current opinion in structural biology.
[91] L. Verlet. Computer "Experiments" on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules , 1967 .
[92] Ausif Mahmood,et al. Review of Deep Learning Algorithms and Architectures , 2019, IEEE Access.
[93] Lei Wang,et al. Discovering phase transitions with unsupervised learning , 2016, 1606.00318.
[94] Martín Carpio,et al. A novel formulation of orthogonal polynomial kernel functions for SVM classifiers: The Gegenbauer family , 2018, Pattern Recognit..
[95] Le Song,et al. On the Complexity of Learning Neural Networks , 2017, NIPS.
[96] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[97] Qisong Xu,et al. Machine Learning for Polymer Swelling in Liquids , 2020 .
[98] Pengfei Zhang,et al. Machine Learning Topological Invariants with Neural Networks , 2017, Physical review letters.
[99] H. Stanley,et al. Dependence of critical properties on dimensionality of spins , 1968 .
[100] Jürgen Schmidhuber,et al. LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[101] Simone Severini,et al. Learning hard quantum distributions with variational autoencoders , 2017, npj Quantum Information.
[102] C. Holm,et al. ESPResSo 4.0 – an extensible software package for simulating soft matter systems , 2018, The European Physical Journal Special Topics.
[103] Akinori Tanaka,et al. Deep learning and the AdS/CFT correspondence , 2018, Physical Review D.
[104] T. C. Lubensky. Soft condensed matter physics , 1997 .
[105] Michael A Webb,et al. Graph-Based Approach to Systematic Molecular Coarse-Graining. , 2019, Journal of chemical theory and computation.
[106] Alfonso Rojas-Domínguez,et al. Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis , 2018, IEEE Access.
[107] D. Mehler,et al. Correction: Open science challenges, benefits and tips in early career and beyond , 2019, PLoS biology.
[108] J. Carrasquilla. Machine learning for quantum matter , 2020, 2003.11040.
[109] Kyle Mills,et al. Deep learning and the Schrödinger equation , 2017, ArXiv.
[110] M. Siegel,et al. Robustness of the Berezinskii-Kosterlitz-Thouless transition in ultrathin NbN films near the superconductor-insulator transition , 2013, 1304.5419.
[111] Volker Roth,et al. The generalized LASSO , 2004, IEEE Transactions on Neural Networks.
[112] Andersen,et al. Scaling behavior in the beta -relaxation regime of a supercooled Lennard-Jones mixture. , 1994, Physical review letters.
[113] Simona Cocco,et al. Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins , 2019, Neural Computation.
[114] Christoph Dellago,et al. Accurate determination of crystal structures based on averaged local bond order parameters. , 2008, The Journal of chemical physics.
[115] Mohammad M. Sultan,et al. Automated design of collective variables using supervised machine learning. , 2018, The Journal of chemical physics.
[116] Demis Hassabis,et al. Improved protein structure prediction using potentials from deep learning , 2020, Nature.
[117] Roger G. Melko,et al. Kernel methods for interpretable machine learning of order parameters , 2017, 1704.05848.
[118] G. Jackson,et al. The effect of dipolar interactions on the liquid crystalline phase transitions of hard spherocylinders with central longitudinal dipoles , 1998 .
[119] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[120] Thomas Blaschke,et al. Application of Generative Autoencoder in De Novo Molecular Design , 2017, Molecular informatics.
[121] Marcus Liwicki,et al. DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments , 2018, 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR).
[122] Lutz Frommberger. Qualitative spatial abstraction in reinforcement learning , 2010 .
[123] Kenny Choo,et al. Two-dimensional frustrated J1−J2 model studied with neural network quantum states , 2019, Physical Review B.
[124] B Seoane,et al. Equilibrium fluid-solid coexistence of hard spheres. , 2012, Physical review letters.
[125] Michele Ceriotti,et al. Unsupervised machine learning in atomistic simulations, between predictions and understanding. , 2019, The Journal of chemical physics.
[126] L. Reven,et al. A dynamic view of self-assembled monolayers. , 2000, Accounts of chemical research.
[127] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[128] Roi Livni,et al. On the Computational Efficiency of Training Neural Networks , 2014, NIPS.
[129] Lei Wang,et al. Neural Network Renormalization Group , 2018, Physical review letters.
[130] Heiko Hoffmann,et al. Kernel PCA for novelty detection , 2007, Pattern Recognit..
[131] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[132] Levent Sagun,et al. The jamming transition as a paradigm to understand the loss landscape of deep neural networks , 2018, Physical review. E.
[133] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[134] Lan Bai,et al. Clustering by twin support vector machine and least square twin support vector classifier with uniform output coding , 2019, Knowl. Based Syst..
[135] Xiao Yan Xu,et al. Symmetry-enforced self-learning Monte Carlo method applied to the Holstein model , 2018, Physical Review B.
[136] P. Pieri,et al. Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machines. , 2019, Physical review. E.
[137] Andrew S. Darmawan,et al. Restricted Boltzmann machine learning for solving strongly correlated quantum systems , 2017, 1709.06475.
[138] David J. Schwab,et al. A high-bias, low-variance introduction to Machine Learning for physicists , 2018, Physics reports.
[139] Pavlo O. Dral,et al. Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.
[140] Christian Trott,et al. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials , 2014, J. Comput. Phys..
[141] Bin Li,et al. Applications of machine learning in drug discovery and development , 2019, Nature Reviews Drug Discovery.
[142] Naftali Tishby,et al. Machine learning and the physical sciences , 2019, Reviews of Modern Physics.
[143] Matthias Troyer,et al. Solving the quantum many-body problem with artificial neural networks , 2016, Science.
[144] Joseph Gomes,et al. MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a , 2017, Chemical science.
[145] D. Carvalho,et al. Real-space mapping of topological invariants using artificial neural networks , 2018, 1801.09655.
[146] Joaquin F. Rodriguez-Nieva,et al. Identifying topological order through unsupervised machine learning , 2018, Nature Physics.
[147] Marko Bohanec,et al. Explaining machine learning models in sales predictions , 2017, Expert Syst. Appl..
[148] D. Mehler,et al. Open science challenges, benefits and tips in early career and beyond , 2018, PLoS biology.
[149] P. Anderson. Basic Notions of Condensed Matter Physics , 1983 .
[150] Zhengdong Cheng,et al. Phase diagram of hard spheres , 2001 .
[151] Wei-Chang Yeh,et al. Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..
[152] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[153] James L. McClelland,et al. What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated , 2016, Trends in Cognitive Sciences.
[154] Peter Nightingale. Finite‐size scaling and phenomenological renormalization (invited) , 1982 .
[155] Angshul Majumdar,et al. AutoImpute: Autoencoder based imputation of single-cell RNA-seq data , 2018, Scientific Reports.
[156] F. Y. Wu. The Potts model , 1982 .
[157] T. Morawietz,et al. How van der Waals interactions determine the unique properties of water , 2016, Proceedings of the National Academy of Sciences.
[158] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[159] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[160] Donald C. Wunsch,et al. A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications , 2019, Neural Networks.
[161] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[162] Jeffrey C Grossman,et al. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. , 2017, Physical review letters.
[163] Hythem Sidky,et al. Learning free energy landscapes using artificial neural networks. , 2017, The Journal of chemical physics.
[164] Daniel R. Reid,et al. SSAGES: Software Suite for Advanced General Ensemble Simulations. , 2018, The Journal of chemical physics.
[165] Peter Wittek,et al. Adversarial Domain Adaptation for Identifying Phase Transitions , 2017, ArXiv.
[166] Ehsan Sadrfaridpour,et al. Engineering fast multilevel support vector machines , 2019, Machine Learning.
[167] Andrea J. Liu,et al. A structural approach to relaxation in glassy liquids , 2015, Nature Physics.
[168] Roger G. Melko,et al. Super-resolving the Ising model with convolutional neural networks , 2018, Physical Review B.
[169] Zohar Ringel,et al. Mutual information, neural networks and the renormalization group , 2017, ArXiv.
[170] Xianli Pan,et al. A Novel Twin Support-Vector Machine With Pinball Loss , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[171] Dong-Ling Deng,et al. Machine Learning Topological States , 2016, 1609.09060.
[172] Yang Qi,et al. Self-learning Monte Carlo method: Continuous-time algorithm , 2017, 1705.06724.
[173] O. Tomi,et al. Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions , 2020 .
[174] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[175] R. Castañeda-Priego,et al. Phase behavior of colloids and proteins in aqueous suspensions: theory and computer simulations. , 2012, The Journal of chemical physics.
[176] M. Hutson. Artificial intelligence faces reproducibility crisis. , 2018, Science.
[177] Bo Tian,et al. Power Electronic Modules , 2018 .
[178] G. Jackson,et al. Chain and ring structures in smectic phases of molecules with transverse dipoles , 1997 .
[179] Hichem Sahbi,et al. Deep representation design from deep kernel networks , 2019, Pattern Recognit..
[180] Sergio Gomez Colmenarejo,et al. Hybrid computing using a neural network with dynamic external memory , 2016, Nature.
[181] D. Thouless,et al. Ordering, metastability and phase transitions in two-dimensional systems , 1973 .
[182] Journal of Chemical Physics , 1932, Nature.
[183] B. A. Lindquist,et al. Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations. , 2018, The Journal of chemical physics.
[184] Bagchi,et al. Computer simulation study of the melting transition in two dimensions. , 1996, Physical review letters.
[185] Ah-Hwee Tan,et al. Integrating Temporal Difference Methods and Self-Organizing Neural Networks for Reinforcement Learning With Delayed Evaluative Feedback , 2008, IEEE Transactions on Neural Networks.
[186] Ling Shao,et al. Dense Invariant Feature-Based Support Vector Ranking for Cross-Camera Person Reidentification , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[187] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[188] W. Kohn. An essay on condensed matter physics in the twentieth century , 1999 .
[189] Li Li,et al. Understanding Machine-learned Density Functionals , 2014, ArXiv.
[190] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[191] Eric N Minor,et al. End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy. , 2019, Soft matter.
[192] P. Steinhardt,et al. Bond-orientational order in liquids and glasses , 1983 .
[193] R. Melko,et al. Machine Learning Phases of Strongly Correlated Fermions , 2016, Physical Review X.
[194] Ge Wang,et al. On Interpretability of Artificial Neural Networks , 2020, ArXiv.
[195] Julio López,et al. Alternative second-order cone programming formulations for support vector classification , 2014, Inf. Sci..
[196] Razvan Pascanu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[197] J. Nelson,et al. Suppression of the Berezinskii-Kosterlitz-Thouless transition in 2D superconductors by macroscopic quantum tunneling. , 2012, Physical review letters.
[198] C H Mak. Large-scale simulations of the two-dimensional melting of hard disks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[199] Yann LeCun,et al. Regularization of Neural Networks using DropConnect , 2013, ICML.
[200] Anil K. Jain,et al. Artificial Neural Networks: A Tutorial , 1996, Computer.
[201] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[202] Roger G. Melko,et al. Machine learning vortices at the Kosterlitz-Thouless transition , 2017, 1710.09842.
[203] Max Tegmark,et al. Why Does Deep and Cheap Learning Work So Well? , 2016, Journal of Statistical Physics.
[204] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[205] Nikolaos Doulamis,et al. Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..
[206] R. Kondor,et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.
[207] J. Cirac,et al. Restricted Boltzmann machines in quantum physics , 2019, Nature Physics.
[208] Tomi Ohtsuki,et al. Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems , 2016, 1610.00462.
[209] Honglak Lee,et al. An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.
[210] Shiliang Sun,et al. Multi-Kernel Online Reinforcement Learning for Path Tracking Control of Intelligent Vehicles , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[211] Yang Zhang,et al. Data Imputation Using Least Squares Support Vector Machines in Urban Arterial Streets , 2009, IEEE Signal Processing Letters.
[212] H. Bourlard,et al. Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.
[213] Stephen Jesse,et al. Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors , 2018, Science Advances.
[214] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[215] Yoshua Bengio,et al. Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.
[216] Alan M. Ferrenberg,et al. Critical behavior of the three-dimensional Ising model: A high-resolution Monte Carlo study. , 1991, Physical review. B, Condensed matter.
[217] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[218] Haiping Huang,et al. Advanced Mean Field Theory of Restricted Boltzmann Machine , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[219] Wonmuk Hwang,et al. Design of nanostructured biological materials through self-assembly of peptides and proteins. , 2002, Current opinion in chemical biology.
[220] M. Fisher,et al. Three-state Potts model and anomalous tricritical points , 1973 .
[221] Yong-Sheng Zhang,et al. Solving frustrated quantum many-particle models with convolutional neural networks , 2018, Physical Review B.
[222] Weili Zeng,et al. Recurrent Neural Networks With External Addressable Long-Term and Working Memory for Learning Long-Term Dependences , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[223] J. Cirac,et al. Neural-Network Quantum States, String-Bond States, and Chiral Topological States , 2017, 1710.04045.
[224] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[225] John B. Kogut,et al. An introduction to lattice gauge theory and spin systems , 1979 .
[226] Wei Jiang,et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks , 2018, Neurocomputing.
[227] Shifei Ding,et al. Discrete space reinforcement learning algorithm based on support vector machine classification , 2018, Pattern Recognit. Lett..
[228] Steven D. Brown,et al. Neural network models of potential energy surfaces , 1995 .
[229] Long Zhang,et al. Dynamical detection of topological charges , 2018, Physical Review A.
[230] Hwee Kuan Lee,et al. Machine-Learning Studies on Spin Models , 2020, Scientific Reports.
[231] Wojciech Samek,et al. Methods for interpreting and understanding deep neural networks , 2017, Digit. Signal Process..
[232] Dongkyu Kim,et al. Smallest neural network to learn the Ising criticality. , 2018, Physical review. E.
[233] TesauroGerald. Practical Issues in Temporal Difference Learning , 1992 .
[234] Ian W. Hamley,et al. Introduction to soft matter: synthetic and biological self-assembling materials. Revised edition , 2007 .
[235] Martin Hilbert,et al. The World’s Technological Capacity to Store, Communicate, and Compute Information , 2011, Science.
[236] T. Ohtsuki,et al. Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions , 2016, 1612.04909.
[237] A. Tanaka,et al. Detection of phase transition via convolutional neural network , 2016, 1609.09087.
[238] A. Fisher,et al. The Theory of Critical Phenomena: An Introduction to the Renormalization Group , 1992 .
[239] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[240] Steve Plimpton,et al. Fast parallel algorithms for short-range molecular dynamics , 1993 .
[241] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[242] Juan Carrasquilla,et al. Machine learning quantum phases of matter beyond the fermion sign problem , 2016, Scientific Reports.
[243] Sebastian Johann Wetzel,et al. Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders , 2017, Physical review. E.
[244] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[245] Ying Xie,et al. Deep Embedding Kernel , 2018, Neurocomputing.
[246] N. Wagner,et al. Dynamical arrest transition in nanoparticle dispersions with short-range interactions. , 2011, Physical review letters.
[247] K. Wilson. Renormalization Group and Critical Phenomena. II. Phase-Space Cell Analysis of Critical Behavior , 1971 .
[248] N. Aluru,et al. Molecular Dynamics Properties without the Full Trajectory: A Denoising Autoencoder Network for Properties of Simple Liquids. , 2019, The journal of physical chemistry letters.
[249] Hong Chen,et al. Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems , 1995, IEEE Trans. Neural Networks.
[250] Tanaka Akinori,et al. Detection of Phase Transition via Convolutional Neural Networks , 2016, 1609.09087.
[251] Derek Hoiem,et al. Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[252] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[253] Razvan Pascanu,et al. Revisiting Natural Gradient for Deep Networks , 2013, ICLR.
[254] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[255] J. Kosterlitz,et al. The critical properties of the two-dimensional xy model , 1974 .
[256] Saeid Nahavandi,et al. Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications , 2018, IEEE Transactions on Cybernetics.
[257] Alán Aspuru-Guzik,et al. Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.
[258] Yang Qi,et al. Self-learning Monte Carlo method and cumulative update in fermion systems , 2017 .