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Machine 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
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
Miniaturizing neural networks for charge state autotuning in quantum dots. 15001 - Anna Dawid
Hessian-based toolbox for reliable and interpretable machine learning in physics. 15002 - Maxim A. Ziatdinov
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. 15003 - Ryan-Rhys Griffiths
Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. 15004 - Kasra Asnaashari
Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules. 15005 - Harold Erbin
Deep multi-task mining Calabi-Yau four-folds. 15006 - Hongyu Shen
Statistically-informed deep learning for gravitational wave parameter estimation. 15007 - Søren Ager Meldgaard, Jonas Köhler
Generating stable molecules using imitation and reinforcement learning. 15008 - Moritz Hoffmann
Deeptime: a Python library for machine learning dynamical models from time series data. 15009 - Mohammadreza Noormandipour
Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model. 15010 - Johannes Gedeon
Machine learning the derivative discontinuity of density-functional theory. 15011 - Jonas Busk
Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks. 15012 - Muhammad Firmansyah Kasim
Building high accuracy emulators for scientific simulations with deep neural architecture search. 15013 - Jose M. Clavijo
Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier *. 15014 - Diogo R. Ferreira
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
Mutual information scaling for tensor network machine learning. 15017 - Samuel Genheden
Fast prediction of distances between synthetic routes with deep learning. 15018 - Maximilian P. Niroomand
Characterising the area under the curve loss function landscape. 15019 - Chenhua Geng
Differentiable programming of isometric tensor networks. 15020 - Stephen B. Menary
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
Graph neural network-based resource allocation strategies for multi-object spectroscopy. 15023 - Nicole Creange
Towards automating structural discovery in scanning transmission electron microscopy *. 15024 - Samuel Yen-Chi Chen
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
Non-perturbative renormalization for the neural network-QFT correspondence. 15027 - Somesh Mohapatra
Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning. 15028 - Markus Fleck
Decoupled coordinates for machine learning-based molecular fragment linking. 15029 - Federica Gerace
Probing transfer learning with a model of synthetic correlated datasets. 15030 - Kadierdan Kaheman
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
Photon detection probability prediction using one-dimensional generative neural network. 15033 - Quoc Chuong Nguyen
Qsun: an open-source platform towards practical quantum machine learning applications. 15034 - Bogdan Kustowski
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
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
Three-body renormalization group limit cycles based on unsupervised feature learning. 25003 - Maximilian P. Niroomand
On the capacity and superposition of minima in neural network loss function landscapes. 25004 - Eri Teruya
ARTS: autonomous research topic selection system using word embeddings and network analysis. 25005 - Rainier Barrett
Simulation-based inference with approximately correct parameters via maximum entropy. 25006 - Massimiliano Lupo Pasini
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *. 25007 - Konstantin T. Matchev
InClass nets: independent classifier networks for nonparametric estimation of conditional independence mixture models and unsupervised classification. 25008 - Peter Wirnsberger
Normalizing flows for atomic solids. 25009 - Juan Yao
Noise enhanced neural networks for analytic continuation. 25010 - Ludwig Winkler
High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks. 25011 - Benedikt Maier
Pile-up mitigation using attention. 25012 - Carlo R. da Cunha
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning. 25013 - Erik Buhmann, Sascha Diefenbacher
Hadrons, better, faster, stronger. 25014 - Narbota Amanova
Explainability for deep learning in mammography image quality assessment. 25015 - Adrian Alan Pol
Lightweight jet reconstruction and identification as an object detection task. 25016
Volume 3, Number 3, September 2022
- Yu-Wei Chang
Neural network training with highly incomplete medical datasets. 35001 - Leander Thiele
Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. 35002 - Mary Touranakou
Particle-based fast jet simulation at the LHC with variational autoencoders. 35003 - Oriel Kiss
Quantum neural networks force fields generation. 35004 - Carlo Lucibello
Deep learning via message passing algorithms based on belief propagation. 35005 - Michelle L. J. Lollie
High-dimensional encryption in optical fibers using spatial modes of light and machine learning. 35006 - Aleksandra Ciprijanovic
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. 35007 - Luca A. Thiede
Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning. 35008 - Hong-Ye Hu
RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior. 35009 - Harsh Bhatia
A biology-informed similarity metric for simulated patches of human cell membrane. 35010 - Sven Krippendorf
A duality connecting neural network and cosmological dynamics. 35011 - Joseph Musielewicz
FINETUNA: fine-tuning accelerated molecular simulations. 3 - James Kahn
Learning tree structures from leaves for particle decay reconstruction. 35012 - Carolina Herrera Segura, Edison Montoya
Subaging in underparametrized deep neural networks. 35013 - Magdalena Larfors
Numerical metrics for complete intersection and Kreuzer-Skarke Calabi-Yau manifolds. 35014 - Raimon Fabregat
Metric learning for kernel ridge regression: assessment of molecular similarity. 35015 - Jayant Jha
Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo. 35016 - Luca Anzalone
Improving parametric neural networks for high-energy physics (and beyond). 35017
Volume 3, Number 4, December 2022
- Sanjaya Lohani
Data-centric machine learning in quantum information science. 4 - Moritz Reh
Variational Monte Carlo approach to partial differential equations with neural networks. 4 - Gourav Khullar
DIGS: deep inference of galaxy spectra with neural posterior estimation. 4 - Rama K. Vasudevan
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model. 4 - Jose M. Munoz
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
Physics-AI symbiosis. 41001 - Muhammad Izzatullah
Laplace HypoPINN: physics-informed neural network for hypocenter localization and its predictive uncertainty. 45001 - Arwen V. Bradley
Shift-curvature, SGD, and generalization. 45002 - Nathan A Garland
Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling. 45003 - Lucas Böttcher
Near-optimal control of dynamical systems with neural ordinary differential equations. 45004 - Puck van Gerwen
Physics-based representations for machine learning properties of chemical reactions. 45005 - Lorenz Vaitl
Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows. 45006 - Sebastian Johann Wetzel
Twin neural network regression is a semi-supervised regression algorithm. 45007 - Lenz Fiedler
Training-free hyperparameter optimization of neural networks for electronic structures in matter. 45008 - Yi Yu
Chemical transformer compression for accelerating both training and inference of molecular modeling. 45009 - Sina Stocker, Johannes Gasteiger
How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? 45010 - Nicolò Ghielmetti
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
Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning. 45013 - Mohammad Tohidi Vahdat
Machine-learning accelerated identification of exfoliable two-dimensional materials. 45014 - Rishikesh Magar
AugLiChem: data augmentation library of chemical structures for machine learning. 45015 - Luis E. Herrera Rodríguez
A comparative study of different machine learning methods for dissipative quantum dynamics. 45016 - Haoyan Huo
Unified representation of molecules and crystals for machine learning. 45017 - Victor Fung
Atomic structure generation from reconstructing structural fingerprints. 45018 - Pallavi Malavath
Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation. 45019 - Sergey N. Pozdnyakov
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
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
Active particles using reinforcement learning to navigate in complex motility landscapes. 45024 - Hendrik Poulsen Nautrup
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
Interaction decompositions for tensor network regression. 45027 - Yuge Hu
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials. 45028 - Woon Hyung Cho
Pixel-wise classification in graphene-detection with tree-based machine learning algorithms. 45029 - Junyu Liu
Towards a variational Jordan-Lee-Preskill quantum algorithm. 45030 - Connor Allen
Optimal data generation for machine learned interatomic potentials. 45031 - Nikhil V. S. Avula
Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids. 45032 - Wonkyeong Lee
No-reference perceptual CT image quality assessment based on a self-supervised learning framework. 45033 - Yuta Suzuki
Self-supervised learning of materials concepts from crystal structures via deep neural networks. 45034 - Amilson R. Fritsch
Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research. 47001
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