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ORCIDMachine Learning: Science and Technology, Volume 3
Volume 3, Number 1, March 2022
- Peter Cha
, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, Eun-Ah Kim:
Attention-based quantum tomography. 01 - Sergei Manzhos, Eita Sasaki, Manabu Ihara:
Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data. 01 - Siddharth Mishra-Sharma:
Inferring dark matter substructure with astrometric lensing beyond the power spectrum. 01 - Stefanie Czischek, Victor Yon, Marc-Antoine Genest, Marc-Antoine Roux, Sophie Rochette, Julien Camirand Lemyre, Mathieu Moras, Michel Pioro-Ladriere, Dominique Drouin, Yann Beilliard, Roger G. Melko:
Miniaturizing neural networks for charge state autotuning in quantum dots. 15001 - Anna Dawid, Patrick Huembeli, Michal Tomza, Maciej Lewenstein, Alexandre Dauphin:
Hessian-based toolbox for reliable and interpretable machine learning in physics. 15002 - Maxim A. Ziatdinov, Ayana Ghosh, Sergei V. Kalinin:
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. 15003 - Ryan-Rhys Griffiths, Alexander A. Aldrick, Miguel Garcia-Ortegon, Vidhi Lalchand, Alpha A. Lee:
Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. 15004 - Kasra Asnaashari, Roman V. Krems:
Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules. 15005 - Harold Erbin, Riccardo Finotello, Robin Schneider, Mohamed Tamaazousti:
Deep multi-task mining Calabi-Yau four-folds. 15006 - Hongyu Shen, E. A. Huerta, Eamonn O'Shea, Prayush Kumar, Zhizhen Zhao:
Statistically-informed deep learning for gravitational wave parameter estimation. 15007 - Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer:
Generating stable molecules using imitation and reinforcement learning. 15008 - Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé:
Deeptime: a Python library for machine learning dynamical models from time series data. 15009 - Mohammadreza Noormandipour, Sun Youran, Babak Haghighat:
Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model. 15010 - Johannes Gedeon, Jonathan Schmidt, Matthew J. P. Hodgson, Jack Wetherell, Carlos L. Benavides-Riveros, Miguel A. L. Marques:
Machine learning the derivative discontinuity of density-functional theory. 15011 - Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, Tejs Vegge:
Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks. 15012 - Muhammad Firmansyah Kasim, Duncan Watson-Parris, Lucia Deaconu, Sophy Oliver, Peter W. Hatfield, Dustin H. Froula, Gianluca Gregori, Matt Jarvis, Samar Khatiwala, Jun Korenaga, Jacob Topp-Mugglestone, Eleonora Viezzer, Sam M. Vinko:
Building high accuracy emulators for scientific simulations with deep neural architecture search. 15013 - Jose M. Clavijo, Paul Glaysher, J. Jitsev, Judith M. Katzy:
Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier *. 15014 - Diogo R. Ferreira, Tiago A. Martins, Paulo Rodrigues:
Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion. 15015 - Artan Sheshmani, Yi-Zhuang You:
Categorical representation learning: morphism is all you need. 15016 - Ian Convy, William J. Huggins, Haoran Liao, K. Birgitta Whaley:
Mutual information scaling for tensor network machine learning. 15017 - Samuel Genheden, Ola Engkvist, Esben Jannik Bjerrum:
Fast prediction of distances between synthetic routes with deep learning. 15018 - Maximilian P. Niroomand, Conor T. Cafolla, John W. R. Morgan, David J. Wales:
Characterising the area under the curve loss function landscape. 15019 - Chenhua Geng, Hong-Ye Hu, Yijian Zou:
Differentiable programming of isometric tensor networks. 15020 - Stephen B. Menary, Darren D. Price:
Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences. 15021 - Ross Irwin, Spyridon Dimitriadis, Jiazhen He, Esben Jannik Bjerrum:
Chemformer: a pre-trained transformer for computational chemistry. 15022 - Tianshu Wang, Peter Melchior:
Graph neural network-based resource allocation strategies for multi-object spectroscopy. 15023 - Nicole Creange, Ondrej Dyck, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin:
Towards automating structural discovery in scanning transmission electron microscopy *. 15024 - Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Hsi-Sheng Goan, Ying-Jer Kao:
Variational quantum reinforcement learning via evolutionary optimization. 15025 - Suryanarayana Maddu, Dominik Sturm, Christian L. Müller, Ivo F. Sbalzarini:
Inverse Dirichlet weighting enables reliable training of physics informed neural networks. 15026 - Harold Erbin, Vincent Lahoche, Dine Ousmane Samary:
Non-perturbative renormalization for the neural network-QFT correspondence. 15027 - Somesh Mohapatra, Joyce An, Rafael Gómez-Bombarelli:
Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning. 15028 - Markus Fleck, Michael G. Müller, Noah Weber, Christopher Trummer:
Decoupled coordinates for machine learning-based molecular fragment linking. 15029 - Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe, Lenka Zdeborová:
Probing transfer learning with a model of synthetic correlated datasets. 15030 - Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz:
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data. 15031 - Carsten G. Staacke, Simon Wengert, Christian Kunkel, Gábor Csányi, Karsten Reuter, Johannes T. Margraf:
Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model. 15032 - Wei Mu, Alexander I. Himmel, Bryan Ramson:
Photon detection probability prediction using one-dimensional generative neural network. 15033 - Quoc Chuong Nguyen, Le Bin Ho, Lan Nguyen Tran, Hung Q. Nguyen:
Qsun: an open-source platform towards practical quantum machine learning applications. 15034 - Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Michael K. G. Kruse, Ryan Nora:
Suppressing simulation bias in multi-modal data using transfer learning. 15035
Volume 3, Number 2, June 2022
- Tahir I. Yusufaly:
Extending the relative seriality formalism for interpretable deep learning of normal tissue complication probability models. 24001 - Eric A. Moreno, Bartlomiej Borzyszkowski, Maurizio Pierini, Jean-Roch Vlimant, Maria Spiropulu:
Source-agnostic gravitational-wave detection with recurrent autoencoders. 25001 - J. C. S. Kadupitiya, Geoffrey C. Fox, Vikram Jadhao:
Solving Newton's equations of motion with large timesteps using recurrent neural networks based operators. 25002 - Bastian Kaspschak, Ulf-G. Meißner:
Three-body renormalization group limit cycles based on unsupervised feature learning. 25003 - Maximilian P. Niroomand, John W. R. Morgan, Conor T. Cafolla, David J. Wales:
On the capacity and superposition of minima in neural network loss function landscapes. 25004 - Eri Teruya, Tadashi Takeuchi, Hidekazu Morita, Takayuki Hayashi, Kanta Ono:
ARTS: autonomous research topic selection system using word embeddings and network analysis. 25005 - Rainier Barrett, Mehrad Ansari, Gourab Ghoshal, Andrew D. White:
Simulation-based inference with approximately correct parameters via maximum entropy. 25006 - Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi:
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *. 25007 - Konstantin T. Matchev, Prasanth Shyamsundar:
InClass nets: independent classifier networks for nonparametric estimation of conditional independence mixture models and unsupervised classification. 25008 - Peter Wirnsberger, George Papamakarios, Borja Ibarz, Sébastien Racanière, Andrew J. Ballard, Alexander Pritzel, Charles Blundell:
Normalizing flows for atomic solids. 25009 - Juan Yao, Ce Wang, Zhiyuan Yao, Hui Zhai:
Noise enhanced neural networks for analytic continuation. 25010 - Ludwig Winkler, Klaus-Robert Müller, Huziel E. Sauceda:
High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks. 25011 - Benedikt Maier, S. M. Narayanan, G. de Castro, Maxim Goncharov, Christoph Paus, Matthias Schott:
Pile-up mitigation using attention. 25012 - Carlo R. da Cunha, Nobuyuki P. Aoki, David K. Ferry, Ying-Cheng Lai:
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning. 25013 - Erik Buhmann, Sascha Diefenbacher, Daniel Hundhausen, Gregor Kasieczka, William Korcari, Engin Eren, Frank Gaede, Katja Krüger, Peter McKeown, Lennart Rustige:
Hadrons, better, faster, stronger. 25014 - Narbota Amanova, Jörg Martin, Clemens Elster:
Explainability for deep learning in mammography image quality assessment. 25015 - Adrian Alan Pol, Thea Aarrestad, Ekaterina Govorkova, Roi Halily, Anat Klempner, Tal Kopetz, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Olya Sirkin, Sioni Summers:
Lightweight jet reconstruction and identification as an object detection task. 25016
Volume 3, Number 3, September 2022
- Yu-Wei Chang, Laura Natali, Oveis Jamialahmadi, Stefano Romeo, Joana B. Pereira, Giovanni Volpe:
Neural network training with highly incomplete medical datasets. 35001 - Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel:
Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. 35002 - Mary Touranakou, Nadezda Chernyavskaya, Javier M. Duarte, Dimitrios Gunopulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant:
Particle-based fast jet simulation at the LHC with variational autoencoders. 35003 - Oriel Kiss, Francesco Tacchino, Sofia Vallecorsa, Ivano Tavernelli:
Quantum neural networks force fields generation. 35004 - Carlo Lucibello, Fabrizio Pittorino, Gabriele Perugini, Riccardo Zecchina:
Deep learning via message passing algorithms based on belief propagation. 35005 - Michelle L. J. Lollie, Fatemeh Mostafavi, Narayan Bhusal, Mingyuan Hong, Chenglong You, Roberto de J. León-Montiel, Omar S. Magaña-Loaiza, Mario Alan Quiroz-Juárez:
High-dimensional encryption in optical fibers using spatial modes of light and machine learning. 35006 - Aleksandra Ciprijanovic, Diana Kafkes, Gregory F. Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild:
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. 35007 - Luca A. Thiede, Mario Krenn, AkshatKumar Nigam, Alán Aspuru-Guzik:
Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning. 35008 - Hong-Ye Hu, Dian Wu, Yi-Zhuang You, Bruno A. Olshausen, Yubei Chen:
RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior. 35009 - Harsh Bhatia, Jayaraman J. Thiagarajan, Rushil Anirudh, T. S. Jayram, Tomas Oppelstrup, Helgi I. Ingólfsson, Felice C. Lightstone, Peer-Timo Bremer:
A biology-informed similarity metric for simulated patches of human cell membrane. 35010 - Sven Krippendorf, Michael Spannowsky:
A duality connecting neural network and cosmological dynamics. 35011 - Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary W. Ulissi:
FINETUNA: fine-tuning accelerated molecular simulations. 3 - James Kahn, Ilias Tsaklidis, Oskar Taubert, Lea Reuter, Giulio Dujany, Tobias Boeckh, Arthur Thaller, Pablo Goldenzweig, Florian Bernlochner, Achim Streit, Markus Götz:
Learning tree structures from leaves for particle decay reconstruction. 35012 - Carolina Herrera Segura, Edison Montoya, Diego Tapias:
Subaging in underparametrized deep neural networks. 35013 - Magdalena Larfors, André Lukas, Fabian Ruehle, Robin Schneider:
Numerical metrics for complete intersection and Kreuzer-Skarke Calabi-Yau manifolds. 35014 - Raimon Fabregat, Puck van Gerwen, Matthieu Haeberle, Friedrich Eisenbrand, Clémence Corminboeuf:
Metric learning for kernel ridge regression: assessment of molecular similarity. 35015 - Jayant Jha, Meysam Hashemi, Anirudh Nihalani Vattikonda, Huifang E. Wang, Viktor K. Jirsa:
Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo. 35016 - Luca Anzalone, Tommaso Diotalevi, Daniele Bonacorsi:
Improving parametric neural networks for high-energy physics (and beyond). 35017
Volume 3, Number 4, December 2022
- Sanjaya Lohani, Joseph M. Lukens, Ryan T. Glasser, Thomas A. Searles, Brian T. Kirby:
Data-centric machine learning in quantum information science. 4 - Moritz Reh, Martin Gärttner:
Variational Monte Carlo approach to partial differential equations with neural networks. 4 - Gourav Khullar, Brian Nord, Aleksandra Ciprijanovic, Jason Poh, Fei Xu:
DIGS: deep inference of galaxy spectra with neural posterior estimation. 4 - Rama K. Vasudevan, Erick Orozco, Sergei V. Kalinin:
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model. 4 - Jose M. Munoz, Ilyes Batatia, Christoph Ortner:
Boost invariant polynomials for efficient jet tagging. 4 - Carl Poelking, Felix A. Faber, Bingqing Cheng:
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale. 40501 - Bahram Jalali, Yiming Zhou, Achuta Kadambi, Vwani Roychowdhury:
Physics-AI symbiosis. 41001 - Muhammad Izzatullah, Isa Eren Yildirim, Umair bin Waheed, Tariq Alkhalifah:
Laplace HypoPINN: physics-informed neural network for hypocenter localization and its predictive uncertainty. 45001 - Arwen V. Bradley, Carlos Alberto Gomez-Uribe, Manish Reddy Vuyyuru:
Shift-curvature, SGD, and generalization. 45002 - Nathan A Garland, Romit Maulik, Qi Tang, Xian-Zhu Tang, Prasanna Balaprakash:
Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling. 45003 - Lucas Böttcher, Thomas Asikis:
Near-optimal control of dynamical systems with neural ordinary differential equations. 45004 - Puck van Gerwen, Alberto Fabrizio, Matthew D. Wodrich, Clémence Corminboeuf:
Physics-based representations for machine learning properties of chemical reactions. 45005 - Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel:
Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows. 45006 - Sebastian Johann Wetzel, Roger G. Melko, Isaac Tamblyn:
Twin neural network regression is a semi-supervised regression algorithm. 45007 - Lenz Fiedler, Nils Hoffmann, Parvez Mohammed, Gabriel A. Popoola, Tamar Yovell, Vladyslav Oles, J. Austin Ellis, Sivasankaran Rajamanickam, Attila Cangi:
Training-free hyperparameter optimization of neural networks for electronic structures in matter. 45008 - Yi Yu, Karl Börjesson:
Chemical transformer compression for accelerating both training and inference of molecular modeling. 45009 - Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf:
How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? 45010 - Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip C. Harris:
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. 45011 - Cristiano Fanelli, James Giroux, Z. Papandreou:
'Flux+Mutability': a conditional generative approach to one-class classification and anomaly detection. 45012 - Jihye Baek, Avice M. O'Connell, Kevin J. Parker:
Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning. 45013 - Mohammad Tohidi Vahdat, Kumar Agrawal Varoon, Giovanni Pizzi:
Machine-learning accelerated identification of exfoliable two-dimensional materials. 45014 - Rishikesh Magar, Yuyang Wang, Cooper Lorsung, Chen Liang, Hariharan Ramasubramanian, Peiyuan Li, Amir Barati Farimani:
AugLiChem: data augmentation library of chemical structures for machine learning. 45015 - Luis E. Herrera Rodríguez, Arif Ullah, Kennet J. Rueda Espinosa, Pavlo O. Dral, Alexei A Kananenka:
A comparative study of different machine learning methods for dissipative quantum dynamics. 45016 - Haoyan Huo, Matthias Rupp:
Unified representation of molecules and crystals for machine learning. 45017 - Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, Panchapakesan Ganesh:
Atomic structure generation from reconstructing structural fingerprints. 45018 - Pallavi Malavath, Nagaraju Devarakonda:
Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation. 45019 - Sergey N. Pozdnyakov, Michele Ceriotti:
Incompleteness of graph neural networks for points clouds in three dimensions. 45020 - L. Storm, Kristian Gustavsson, Bernhard Mehlig:
Constraints on parameter choices for successful time-series prediction with echo-state networks. 45021 - Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Peter Bjørn Jørgensen, Ole Winther:
NeuralNEB - neural networks can find reaction paths fast. 45022 - W. Huang, Amanda S. Barnard:
Federated data processing and learning for collaboration in the physical sciences. 45023 - Paul A. Monderkamp, Fabian Jan Schwarzendahl, Michael Andreas Klatt, Hartmut Löwen:
Active particles using reinforcement learning to navigate in complex motility landscapes. 45024 - Hendrik Poulsen Nautrup, Tony Metger, Raban Iten, Sofiène Jerbi, Lea M. Trenkwalder, Henrik Wilming, Hans J. Briegel, Renato Renner:
Operationally meaningful representations of physical systems in neural networks. 45025 - Stephen Whitelam, Viktor Selin, Ian Benlolo, Corneel Casert, Isaac Tamblyn:
Training neural networks using Metropolis Monte Carlo and an adaptive variant. 45026 - Ian Convy, K. Birgitta Whaley:
Interaction decompositions for tensor network regression. 45027 - Yuge Hu, Joseph Musielewicz, Zachary W. Ulissi, Andrew J. Medford:
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials. 45028 - Woon Hyung Cho, Jiseon Shin, Young Duck Kim, George J. Jung:
Pixel-wise classification in graphene-detection with tree-based machine learning algorithms. 45029 - Junyu Liu, Zimu Li, Han Zheng, Xiao Yuan, Jinzhao Sun:
Towards a variational Jordan-Lee-Preskill quantum algorithm. 45030 - Connor Allen, Albert P. Bartók:
Optimal data generation for machine learned interatomic potentials. 45031 - Nikhil V. S. Avula, Shivanand K. Veesam, Sudarshan Behera, Sundaram Balasubramanian:
Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids. 45032 - Wonkyeong Lee, Eunbyeol Cho, Wonjin Kim, Hyebin Choi, Kyongmin Sarah Beck, Hyun Jung Yoon, Jongduk Baek, Jang-Hwan Choi:
No-reference perceptual CT image quality assessment based on a self-supervised learning framework. 45033 - Yuta Suzuki, Tatsunori Taniai, Kotaro Saito, Yoshitaka Ushiku, Kanta Ono:
Self-supervised learning of materials concepts from crystal structures via deep neural networks. 45034 - Amilson R. Fritsch, Shangjie Guo, Sophia M. Koh, Ian B. Spielman, Justyna P. Zwolak:
Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research. 47001
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