Related references
Note: Only part of the references are listed.Fundamental limits of symmetric low-rank matrix estimation
Marc Lelarge et al.
PROBABILITY THEORY AND RELATED FIELDS (2019)
Quantum Entanglement in Deep Learning Architectures
Yoav Levine et al.
PHYSICAL REVIEW LETTERS (2019)
Optimal errors and phase transitions in high-dimensional generalized linear models
Jean Barbier et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Quantifying Hidden Order out of Equilibrium
Stefano Martiniani et al.
PHYSICAL REVIEW X (2019)
Active learning of uniformly accurate interatomic potentials for materials simulation
Linfeng Zhang et al.
PHYSICAL REVIEW MATERIALS (2019)
A continuous analogue of the tensor-train decomposition
Alex Gorodetsky et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)
Machine learning in electronic-quantum-matter imaging experiments
Yi Zhang et al.
NATURE (2019)
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noe et al.
SCIENCE (2019)
Phase transition encoded in neural network
Kouji Kashiwa et al.
PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS (2019)
An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations
Thomas J. Armitage et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2019)
Pattern Recognition Techniques for Boson Sampling Validation
Iris Agresti et al.
PHYSICAL REVIEW X (2019)
Energy flow networks: deep sets for particle jets
Patrick T. Komiske et al.
JOURNAL OF HIGH ENERGY PHYSICS (2019)
Machine learning techniques for state recognition and auto-tuning in quantum dots
Sandesh S. Kalantre et al.
NPJ QUANTUM INFORMATION (2019)
Reconstructing quantum states with generative models
Juan Carrasquilla et al.
NATURE MACHINE INTELLIGENCE (2019)
Interpretable machine learning study of the many-body localization transition in disordered quantum Ising spin chains
Wei Zhang et al.
PHYSICAL REVIEW B (2019)
Probing hidden spin order with interpretable machine learning
Jonas Greitemann et al.
PHYSICAL REVIEW B (2019)
Advantages of versatile neural-network decoding for topological codes
Nishad Maskara et al.
PHYSICAL REVIEW A (2019)
Extensive deep neural networks for transferring small scale learning to large scale systems
Kyle Mills et al.
CHEMICAL SCIENCE (2019)
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
M. S. Albergo et al.
PHYSICAL REVIEW D (2019)
Investigating ultrafast quantum magnetism with machine learning
Giammarco Fabiani et al.
SCIPOST PHYSICS (2019)
Learning multiple order parameters with interpretable machines
Ke Liu et al.
PHYSICAL REVIEW B (2019)
CMU DeepLens: deep learning for automatic image-based galaxy-galaxy strong lens finding
Francois Lanusse et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2018)
Information-theoretic thresholds from the cavity method
Amin Coja-Oghlan et al.
ADVANCES IN MATHEMATICS (2018)
Many-body localization: An introduction and selected topics
Fabien Alet et al.
COMPTES RENDUS PHYSIQUE (2018)
Learning models for electron densities with Bayesian regression
Eric Schmidt et al.
COMPUTATIONAL MATERIALS SCIENCE (2018)
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Tristan Bereau et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Learning free energy landscapes using artificial neural networks
Hythem Sidky et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Hierarchical modeling of molecular energies using a deep neural network
Nicholas Lubbers et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
SchNet - A deep learning architecture for molecules and materials
K. T. Schuett et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Thuong T. Nguyen et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
Ryo Nagai et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy
Aditya Kamath et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry
Matthias Rupp et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Less is more: Sampling chemical space with active learning
Justin S. Smith et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Can exact conditions improve machine-learned density functionals?
Jacob Hollingsworth et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Solid harmonic wavelet scattering for predictions of molecule properties
Michael Eickenberg et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics
Volker L. Deringer et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)
Unifying neural-network quantum states and correlator product states via tensor networks
Stephen R. Clark
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL (2018)
Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice
Hiroki Saito et al.
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2018)
Method to Solve Quantum Few-Body Problems with Artificial Neural Networks
Hiroki Saito
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2018)
Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials
Gabriele C. Sosso et al.
MOLECULAR SIMULATION (2018)
Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
Justin Alsing et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2018)
Equivalent-accuracy accelerated neural-network training using analogue memory
Stefano Ambrogio et al.
NATURE (2018)
Mutual information, neural networks and the renormalization group
Maciej Koch-Janusz et al.
NATURE PHYSICS (2018)
Neural-network quantum state tomography
Giacomo Torlai et al.
NATURE PHYSICS (2018)
Statistical physics and representations in real and artificial neural networks
S. Cocco et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2018)
Latent Space Purification via Neural Density Operators
Giacomo Torlai et al.
PHYSICAL REVIEW LETTERS (2018)
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
Jaideep Pathak et al.
PHYSICAL REVIEW LETTERS (2018)
Machine Learning Topological Invariants with Neural Networks
Pengfei Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
Machine Learning Out-of-Equilibrium Phases of Matter
Jordan Venderley et al.
PHYSICAL REVIEW LETTERS (2018)
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
Michela Paganini et al.
PHYSICAL REVIEW LETTERS (2018)
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
Active learning machine learns to create new quantum experiments
Alexey A. Melnikov et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Quantum machine learning: a classical perspective
Carlo Ciliberto et al.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2018)
v Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Vedran Dunjko et al.
REPORTS ON PROGRESS IN PHYSICS (2018)
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Kun Yao et al.
CHEMICAL SCIENCE (2018)
Mapping uncharted territory in ice from zeolite networks to ice structures
Edgar A. Engel et al.
NATURE COMMUNICATIONS (2018)
An equation-of-state-meter of quantum chromodynamics transition from deep learning
Long-Gang Pang et al.
NATURE COMMUNICATIONS (2018)
VAMPnets for deep learning of molecular kinetics
Andreas Mardt et al.
NATURE COMMUNICATIONS (2018)
Machine learning of frustrated classical spin models (II): Kernel principal component analysis
Ce Wang et al.
FRONTIERS OF PHYSICS (2018)
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
Ivan Glasser et al.
PHYSICAL REVIEW X (2018)
Classification and Geometry of General Perceptual Manifolds
SueYeon Chung et al.
PHYSICAL REVIEW X (2018)
Unsupervised Generative Modeling Using Matrix Product States
Zhao-Yu Han et al.
PHYSICAL REVIEW X (2018)
Energy flow polynomials: a complete linear basis for jet substructure
Patrick T. Komiske et al.
JOURNAL OF HIGH ENERGY PHYSICS (2018)
Learning hard quantum distributions with variational autoencoders
Andrea Rocchetto et al.
NPJ QUANTUM INFORMATION (2018)
Fast inference of deep neural networks in FPGAs for particle physics
J. Duarte et al.
JOURNAL OF INSTRUMENTATION (2018)
Discovering a Transferable Charge Assignment Model Using Machine Learning
Andrew E. Sifain et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)
Machine learning assisted readout of trapped-ion qubits
Alireza Seif et al.
JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS (2018)
Machine learning for molecular and materials science
Keith T. Butler et al.
NATURE (2018)
Machine learning at the energy and intensity frontiers of particle physics
Alexander Radovic et al.
NATURE (2018)
Glider soaring via reinforcement learning in the field
Gautam Reddy et al.
NATURE (2018)
Constraining Effective Field Theories with Machine Learning
Johann Brehmer et al.
PHYSICAL REVIEW LETTERS (2018)
All-optical machine learning using diffractive deep neural networks
Xing Lin et al.
SCIENCE (2018)
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela et al.
NATURE COMMUNICATIONS (2018)
Reinforcement Learning in Different Phases of Quantum Control
Marin Bukov et al.
PHYSICAL REVIEW X (2018)
Reinforcement Learning with Neural Networks for Quantum Feedback
Thomas Foesel et al.
PHYSICAL REVIEW X (2018)
Deep Learning and Its Application to LHC Physics
Dan Guest et al.
ANNUAL REVIEW OF NUCLEAR AND PARTICLE SCIENCE, VOL 68 (2018)
Modelling non-markovian quantum processes with recurrent neural networks
Leonardo Banchi et al.
NEW JOURNAL OF PHYSICS (2018)
Neural Network Renormalization Group
Shuo-Hui Li et al.
PHYSICAL REVIEW LETTERS (2018)
Machine-Learning-Assisted Many-Body Entanglement Measurement
Johnnie Gray et al.
PHYSICAL REVIEW LETTERS (2018)
Symmetries and Many-Body Excitations with Neural-Network Quantum States
Kenny Choo et al.
PHYSICAL REVIEW LETTERS (2018)
Machine Learning Many-Body Localization: Search for the Elusive Nonergodic Metal
Yi-Ting Hsu et al.
PHYSICAL REVIEW LETTERS (2018)
Chemical shifts in molecular solids by machine learning
Federico M. Paruzzo et al.
NATURE COMMUNICATIONS (2018)
Constructing exact representations of quantum many-body systems with deep neural networks
Giuseppe Carleo et al.
NATURE COMMUNICATIONS (2018)
Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
Eric W. Tramel et al.
PHYSICAL REVIEW X (2018)
Machine Learning a General-Purpose Interatomic Potential for Silicon
Albert P. Bartok et al.
PHYSICAL REVIEW X (2018)
Generalized convex hull construction for materials discovery
Andrea Anelli et al.
PHYSICAL REVIEW MATERIALS (2018)
Analytic continuation via domain knowledge free machine learning
Hongkee Yoon et al.
PHYSICAL REVIEW B (2018)
Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator
Marin Bukov
PHYSICAL REVIEW B (2018)
Deep learning and holographic QCD
Koji Hashimoto et al.
PHYSICAL REVIEW D (2018)
Machine learning action parameters in lattice quantum chromodynamics
Phiala E. Shanahan et al.
PHYSICAL REVIEW D (2018)
Many-body localization and delocalization in large quantum chains
Elmer V. H. Doggen et al.
PHYSICAL REVIEW B (2018)
Supervised learning approach for recognizing magnetic skyrmion phases
I. A. Iakovlev et al.
PHYSICAL REVIEW B (2018)
Learning relevant features of data with multi-scale tensor networks
E. Miles Stoudenmire
QUANTUM SCIENCE AND TECHNOLOGY (2018)
Automatic physical inference with information maximizing neural networks
Tom Charnock et al.
PHYSICAL REVIEW D (2018)
Identifying quantum phase transitions with adversarial neural networks
Patrick Huembeli et al.
PHYSICAL REVIEW B (2018)
A guide to constraining effective field theories with machine learning
Johann Brehmer et al.
PHYSICAL REVIEW D (2018)
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson et al.
18TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH (ACAT2017) (2018)
Trochoidal motion and pair generation in skyrmion and antiskyrmion dynamics under spin-orbit torques
Ulrike Ritzmann et al.
NATURE ELECTRONICS (2018)
Decoding small surface codes with feedforward neural networks
Savvas Varsamopoulos et al.
QUANTUM SCIENCE AND TECHNOLOGY (2018)
Learning phase transitions from dynamics
Evert van Nieuwenburg et al.
PHYSICAL REVIEW B (2018)
Quenches near Ising quantum criticality as a challenge for artificial neural networks
Stefanie Czischek et al.
PHYSICAL REVIEW B (2018)
Learning to classify from impure samples with high-dimensional data
Patrick T. Komiske et al.
PHYSICAL REVIEW D (2018)
CALOGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
Michela Paganini et al.
PHYSICAL REVIEW D (2018)
Machine learning vortices at the Kosterlitz-Thouless transition
Matthew J. S. Beach et al.
PHYSICAL REVIEW B (2018)
Quantum dynamics in transverse-field Ising models from classical networks
Markus Schmitt et al.
SCIPOST PHYSICS (2018)
Symmetry-enforced self-learning Monte Carlo method applied to the Holstein model
Chuang Chen et al.
PHYSICAL REVIEW B (2018)
Equivalence of restricted Boltzmann machines and tensor network states
Jing Chen et al.
PHYSICAL REVIEW B (2018)
Machine-learning quantum mechanics: Solving quantum mechanics problems using radial basis function networks
Peiyuan Teng
PHYSICAL REVIEW E (2018)
Deep learning and the AdS/CFT correspondence
Koji Hashimoto et al.
PHYSICAL REVIEW D (2018)
Approximating quantum many-body wave functions using artificial neural networks
Zi Cai et al.
PHYSICAL REVIEW B (2018)
Chiral topological phases from artificial neural networks
Raphael Kaubruegger et al.
PHYSICAL REVIEW B (2018)
Deep learning topological invariants of band insulators
Ning Sun et al.
PHYSICAL REVIEW B (2018)
Solving frustrated quantum many-particle models with convolutional neural networks
Xiao Liang et al.
PHYSICAL REVIEW B (2018)
Machine-learning-assisted correction of correlated qubit errors in a topological code
P. Baireuther et al.
QUANTUM (2018)
Phase diagram of restricted Boltzmann machines and generalized Hopfield networks with arbitrary priors
Adriano Barra et al.
PHYSICAL REVIEW E (2018)
Matrix product operators for sequence-to-sequence learning
Chu Guo et al.
PHYSICAL REVIEW E (2018)
The redshift evolution of massive galaxy clusters in the MACSIS simulations
David J. Barnes et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2017)
Relationship between local structure and relaxation in out-of-equilibrium glassy systems
Samuel S. Schoenholz et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2017)
Spectral dynamics of learning in restricted Boltzmann machines
A. Decelle et al.
EPL (2017)
Geometric Deep Learning Going beyond Euclidean data
Michael M. Bronstein et al.
IEEE SIGNAL PROCESSING MAGAZINE (2017)
Sample-Optimal Tomography of Quantum States
Jeongwan Haah et al.
IEEE TRANSACTIONS ON INFORMATION THEORY (2017)
Projected regression method for solving Fredholm integral equations arising in the analytic continuation problem of quantum physics
Louis-Francois Arsenault et al.
INVERSE PROBLEMS (2017)
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Felix A. Faber et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)
Event generator tuning using Bayesian optimization
P. Ilten et al.
JOURNAL OF INSTRUMENTATION (2017)
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
R. Acciarri et al.
JOURNAL OF INSTRUMENTATION (2017)
PCA Meets RG
Serena Bradde et al.
JOURNAL OF STATISTICAL PHYSICS (2017)
Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions
Tomi Ohtsuki et al.
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2017)
Solving the Bose-Hubbard Model with Machine Learning
Hiroki Saito
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2017)
Detection of Phase Transition via Convolutional Neural Networks
Akinori Tanaka et al.
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2017)
Fast automated analysis of strong gravitational lenses with convolutional neural networks
Yashar D. Hezaveh et al.
NATURE (2017)
Deep learning with coherent nanophotonic circuits
Yichen Shen et al.
NATURE PHOTONICS (2017)
Efficient tomography of a quantum many-body system
B. P. Lanyon et al.
NATURE PHYSICS (2017)
Learning phase transitions by confusion
Evert P. L. van Nieuwenburg et al.
NATURE PHYSICS (2017)
Machine learning phases of matter
Juan Carrasquilla et al.
NATURE PHYSICS (2017)
Energy landscapes for machine learning
Andrew J. Ballard et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2017)
Emergence of Compositional Representations in Restricted Boltzmann Machines
J. Tubiana et al.
PHYSICAL REVIEW LETTERS (2017)
Quantum Loop Topography for Machine Learning
Yi Zhang et al.
PHYSICAL REVIEW LETTERS (2017)
Neural Decoder for Topological Codes
Giacomo Torlai et al.
PHYSICAL REVIEW LETTERS (2017)
Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces
Elia Schneider et al.
PHYSICAL REVIEW LETTERS (2017)
Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo et al.
SCIENCE (2017)
Machine learning molecular dynamics for the simulation of infrared spectra
Michael Gastegger et al.
CHEMICAL SCIENCE (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (2017)
Efficient representation of quantum many-body states with deep neural networks
Xun Gao et al.
NATURE COMMUNICATIONS (2017)
Prediction and real-time compensation of qubit decoherence via machine learning
Sandeep Mavadia et al.
NATURE COMMUNICATIONS (2017)
Bypassing the Kohn-Sham equations with machine learning
Felix Brockherde et al.
NATURE COMMUNICATIONS (2017)
Experimental quantum compressed sensing for a seven-qubit system
C. A. Riofrio et al.
NATURE COMMUNICATIONS (2017)
Quantum Entanglement in Neural Network States
Dong-Ling Deng et al.
PHYSICAL REVIEW X (2017)
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Marcello Benedetti et al.
PHYSICAL REVIEW X (2017)
Machine learning in the string landscape
Jonathan Carifio et al.
JOURNAL OF HIGH ENERGY PHYSICS (2017)
Classification without labels: learning from mixed samples in high energy physics
Eric M. Metodiev et al.
JOURNAL OF HIGH ENERGY PHYSICS (2017)
Deep Neural Network Probabilistic Decoder for Stabilizer Codes
Stefan Krastanov et al.
SCIENTIFIC REPORTS (2017)
Machine learning quantum phases of matter beyond the fermion sign problem
Peter Broecker et al.
SCIENTIFIC REPORTS (2017)
Inverse statistical problems: from the inverse Ising problem to data science
H. Chau Nguyen et al.
ADVANCES IN PHYSICS (2017)
Machine learning Z2 quantum spin liquids with quasiparticle statistics
Yi Zhang et al.
PHYSICAL REVIEW B (2017)
Machine learning of frustrated classical spin models. I. Principal component analysis
Ce Wang et al.
PHYSICAL REVIEW B (2017)
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
Jaideep Pathak et al.
CHAOS (2017)
Restricted Boltzmann machine learning for solving strongly correlated quantum systems
Yusuke Nomura et al.
PHYSICAL REVIEW B (2017)
Machine learning topological states
Dong-Ling Deng et al.
PHYSICAL REVIEW B (2017)
Decorrelated jet substructure tagging using adversarial neural networks
Chase Shimmin et al.
PHYSICAL REVIEW D (2017)
Self-learning Monte Carlo method: Continuous-time algorithm
Yuki Nagai et al.
PHYSICAL REVIEW B (2017)
Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders
Sebastian J. Wetzel
PHYSICAL REVIEW E (2017)
Probing many-body localization with neural networks
Frank Schindler et al.
PHYSICAL REVIEW B (2017)
Self-learning Monte Carlo method and cumulative update in fermion systems
Junwei Liu et al.
PHYSICAL REVIEW B (2017)
Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination
Wenjian Hu et al.
PHYSICAL REVIEW E (2017)
Mean-field message-passing equations in the Hopfield model and its generalizations
Marc Mezard
PHYSICAL REVIEW E (2017)
Self-learning Monte Carlo method
Junwei Liu et al.
PHYSICAL REVIEW B (2017)
Accelerated Monte Carlo simulations with restricted Boltzmann machines
Li Huang et al.
PHYSICAL REVIEW B (2017)
Understanding machine-learned density functionals
Li Li et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2016)
Statistical physics of inference: thresholds and algorithms
Lenka Zdeborová et al.
ADVANCES IN PHYSICS (2016)
Parameterized neural networks for high-energy physics
Pierre Baldi et al.
EUROPEAN PHYSICAL JOURNAL C (2016)
Phase Transitions and Sample Complexity in Bayes-Optimal Matrix Factorization
Yoshiyuki Kabashima et al.
IEEE TRANSACTIONS ON INFORMATION THEORY (2016)
Perspective: Machine learning potentials for atomistic simulations
Joerg Behler
JOURNAL OF CHEMICAL PHYSICS (2016)
A convolutional neural network neutrino event classifier
A. Aurisano et al.
JOURNAL OF INSTRUMENTATION (2016)
Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
Tomoki Ohtsuki et al.
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2016)
The theory of variational hybrid quantum-classical algorithms
Jarrod R. McClean et al.
NEW JOURNAL OF PHYSICS (2016)
Automated Search for new Quantum Experiments
Mario Krenn et al.
PHYSICAL REVIEW LETTERS (2016)
Learning to soar in turbulent environments
Gautam Reddy et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes
Carlo Baldassi et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
Jet-images - deep learning edition
Luke de Oliveira et al.
JOURNAL OF HIGH ENERGY PHYSICS (2016)
Optimal design for universal multiport interferometers
William R. Clements et al.
OPTICA (2016)
Fast machine-learning online optimization of ultra-cold-atom experiments
P. B. Wigley et al.
SCIENTIFIC REPORTS (2016)
Jet substructure classification in high-energy physics with deep neural networks
Pierre Baldi et al.
PHYSICAL REVIEW D (2016)
Discovering phase transitions with unsupervised learning
Lei Wang
PHYSICAL REVIEW B (2016)
Jet flavor classification in high-energy physics with deep neural networks
Daniel Guest et al.
PHYSICAL REVIEW D (2016)
PICS: SIMULATIONS OF STRONG GRAVITATIONAL LENSING IN GALAXY CLUSTERS
Nan Li et al.
ASTROPHYSICAL JOURNAL (2016)
DYNAMICAL MASS MEASUREMENTS OF CONTAMINATED GALAXY CLUSTERS USING MACHINE LEARNING
M. Ntampaka et al.
ASTROPHYSICAL JOURNAL (2016)
Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing
C. Bonnett et al.
PHYSICAL REVIEW D (2016)
New approaches for boosting to uniformity
A. Rogozhnikov et al.
JOURNAL OF INSTRUMENTATION (2015)
Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
E. D. Cubuk et al.
PHYSICAL REVIEW LETTERS (2015)
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
Carlo Baldassi et al.
PHYSICAL REVIEW LETTERS (2015)
Adaptive quantum computation in changing environments using projective simulation
M. Tiersch et al.
SCIENTIFIC REPORTS (2015)
Parton distributions for the LHC run II
Richard D. Ball et al.
JOURNAL OF HIGH ENERGY PHYSICS (2015)
COSMOABC: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
E. E. O. Ishida et al.
ASTRONOMY AND COMPUTING (2015)
LHCb Topological Trigger Reoptimization
Tatiana Likhomanenko et al.
21ST INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP2015), PARTS 1-9 (2015)
THE POPULATION OF GALAXY-GALAXY STRONG LENSES IN FORTHCOMING OPTICAL IMAGING SURVEYS
Thomas E. Collett
ASTROPHYSICAL JOURNAL (2015)
A MACHINE LEARNING APPROACH FOR DYNAMICAL MASS MEASUREMENTS OF GALAXY CLUSTERS
M. Ntampaka et al.
ASTROPHYSICAL JOURNAL (2015)
Constraining the thick disc formation scenario of the Milky Way
A. C. Robin et al.
ASTRONOMY & ASTROPHYSICS (2014)
An Iterative Construction of Solutions of the TAP Equations for the Sherrington-Kirkpatrick Model
Erwin Bolthausen
COMMUNICATIONS IN MATHEMATICAL PHYSICS (2014)
Quantum principal component analysis
Seth Lloyd et al.
NATURE PHYSICS (2014)
A strategy for quantum algorithm design assisted by machine learning
Jeongho Bang et al.
NEW JOURNAL OF PHYSICS (2014)
Searching for exotic particles in high-energy physics with deep learning
P. Baldi et al.
NATURE COMMUNICATIONS (2014)
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan et al.
SCIENTIFIC DATA (2014)
Statistical mechanics of dictionary learning
Ayaka Sakata et al.
EPL (2013)
uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers
J. Stevens et al.
JOURNAL OF INSTRUMENTATION (2013)
Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
V. V. Gligorov et al.
JOURNAL OF INSTRUMENTATION (2013)
TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests
Matias Carrasco Kind et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2013)
On representing chemical environments
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
Spectral redemption in clustering sparse networks
Florent Krzakala et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2013)
emcee: The MCMC Hammer
Daniel Foreman-Mackey et al.
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC (2013)
State evolution for general approximate message passing algorithms, with applications to spatial coupling
Adel Javanmard et al.
INFORMATION AND INFERENCE-A JOURNAL OF THE IMA (2013)
Approximate Bayesian Computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift
E. Cameron et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2012)
Finding Density Functionals with Machine Learning
John C. Snyder et al.
PHYSICAL REVIEW LETTERS (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
Approximate Bayesian computational methods
Jean-Michel Marin et al.
STATISTICS AND COMPUTING (2012)
Localization and Glassy Dynamics Of Many-Body Quantum Systems
Giuseppe Carleo et al.
SCIENTIFIC REPORTS (2012)
Detecting hidden spatial and spatio-temporal structures in glasses and complex physical systems by multiresolution network clustering
P. Ronhovde et al.
EUROPEAN PHYSICAL JOURNAL E (2011)
Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
Aurelien Decelle et al.
PHYSICAL REVIEW E (2011)
Inference and Phase Transitions in the Detection of Modules in Sparse Networks
Aurelien Decelle et al.
PHYSICAL REVIEW LETTERS (2011)
TENSOR-TRAIN DECOMPOSITION
I. V. Oseledets
SIAM JOURNAL ON SCIENTIFIC COMPUTING (2011)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Permutationally Invariant Quantum Tomography
G. Toth et al.
PHYSICAL REVIEW LETTERS (2010)
Quantum State Tomography via Compressed Sensing
David Gross et al.
PHYSICAL REVIEW LETTERS (2010)
Community detection in graphs
Santo Fortunato
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS (2010)
AUTOMATED DETECTION OF GALAXY-SCALE GRAVITATIONAL LENSES IN HIGH-RESOLUTION IMAGING DATA
Philip J. Marshall et al.
ASTROPHYSICAL JOURNAL (2009)
Unsupervised Multiway Data Analysis: A Literature Survey
Evrim Acar et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2009)
A New Scheme for the Tensor Representation
W. Hackbusch et al.
JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS (2009)
On Consistency and Sparsity for Principal Components Analysis in High Dimensions
Iain M. Johnstone et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2009)
Ground-state properties of quantum many-body systems: entangled-plaquette states and variational Monte Carlo
F. Mezzacapo et al.
NEW JOURNAL OF PHYSICS (2009)
Approximating strongly correlated wave functions with correlator product states
Hitesh J. Changlani et al.
PHYSICAL REVIEW B (2009)
Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems
F. Verstraete et al.
ADVANCES IN PHYSICS (2008)
Kernel methods in machine learning
Thomas Hofmann et al.
ANNALS OF STATISTICS (2008)
EAZY: A FAST, PUBLIC PHOTOMETRIC REDSHIFT CODE
Gabriel B. Brammer et al.
ASTROPHYSICAL JOURNAL (2008)
Simulation of quantum many-body systems with strings of operators and Monte Carlo tensor contractions
Norbert Schuch et al.
PHYSICAL REVIEW LETTERS (2008)
The learnability of quantum states
Scott Aaronson
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2007)
A tutorial on spectral clustering
Ulrike von Luxburg
STATISTICS AND COMPUTING (2007)
Entanglement renormalization
G. Vidal
PHYSICAL REVIEW LETTERS (2007)
A systematic search for high surface brightness giant arcs in a Sloan Digital Sky Survey cluster sample
J. Estrada et al.
ASTROPHYSICAL JOURNAL (2007)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Sequential Monte Carlo without likelihoods
S. A. Sisson et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)
The Zurich Extragalactic Bayesian Redshift Analyzer and its first application: COSMOS
R. Feldmann et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2006)
Classical simulation of quantum many-body systems with a tree tensor network
Y. -Y. Shi et al.
PHYSICAL REVIEW A (2006)
Compressed sensing
DL Donoho
IEEE TRANSACTIONS ON INFORMATION THEORY (2006)
The history of analog computing - Introduction to the special section
KH Lundberg
IEEE CONTROL SYSTEMS MAGAZINE (2005)
Boosted decision trees as an alternative to artificial neural networks for particle identification
BP Roe et al.
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT (2005)
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
H Jaeger et al.
SCIENCE (2004)
ANNz:: Estimating photometric redshifts using artificial neural networks
AA Collister et al.
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC (2004)
Markov chain Monte Carlo without likelihoods
P Marjoram et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2003)
Estimating photometric redshifts with artificial neural networks
AE Firth et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2003)
Training products of experts by minimizing contrastive divergence
GE Hinton
NEURAL COMPUTATION (2002)
Latent heat calculation of the three-dimensional q=3, 4, and 5 Potts models by the tensor product variational approach -: art. no. 046702
A Gendiar et al.
PHYSICAL REVIEW E (2002)
Bayesian photometric redshift estimation
N Benítez
ASTROPHYSICAL JOURNAL (2000)
Share Paper
