Active and transfer learning with partially Bayesian neural networks for materials and chemicals | Paper Citation Record

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This paper cites Active learning with statistical models.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Active learning with statistical models

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This paper cites Active Learning Literature Survey.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Active Learning Literature Survey

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This paper cites Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys

Reference 3

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This paper cites Benchmarking active learning strategies for materials optimization and discovery.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Benchmarking active learning strategies for materials optimization and discovery

Reference 5

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This paper cites Small data machine learning in materials science.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Small data machine learning in materials science

Reference 6

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This paper cites Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Reference 7

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This paper cites Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Reference 8

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This paper cites Phase Stability Through Machine Learning.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Phase Stability Through Machine Learning

Reference 9

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This paper cites Data-driven analysis and prediction of stable phases for high-entropy alloy design.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Data-driven analysis and prediction of stable phases for high-entropy alloy design

Reference 10

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This paper cites A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

Reference 11

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This paper cites Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors

Reference 12

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This paper cites Predicting lattice thermal conductivity via machine learning: a mini review.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Predicting lattice thermal conductivity via machine learning: a mini review

Reference 13

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This paper cites Interpretable Machine Learning Model on Thermal Conductivity Using Publicly Available Datasets and Our Internal Lab Dataset.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Interpretable Machine Learning Model on Thermal Conductivity Using Publicly Available Datasets and Our Internal Lab Dataset

Reference 14

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This paper cites Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling

Reference 15

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This paper cites Prediction of glass transition temperature of oxide glasses based on interpretable machine learning and sparse data sets.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Prediction of glass transition temperature of oxide glasses based on interpretable machine learning and sparse data sets

Reference 16

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This paper cites Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses

Reference 17

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This paper cites Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data

Reference 18

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This paper cites Predicting glass transition temperature and melting point of organic compounds via machine learning and molecular embeddings.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Predicting glass transition temperature and melting point of organic compounds via machine learning and molecular embeddings

Reference 19

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This paper cites Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers

Reference 20

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This paper cites Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach

Reference 21

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This paper cites Machine learning dielectric screening for the simulation of excited state properties of molecules and materials.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Reference 22

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This paper cites Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

Reference 23

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This paper cites Machine learning and atomistic origin of high dielectric permittivity in oxides.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Machine learning and atomistic origin of high dielectric permittivity in oxides

Reference 24

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This paper cites Opportunities and Challenges for Machine Learning in Materials Science.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Opportunities and Challenges for Machine Learning in Materials Science

Reference 25

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This paper cites Advances of machine learning in materials science: Ideas and techniques.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Advances of machine learning in materials science: Ideas and techniques

Reference 26

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This paper cites Explainable machine learning in materials science.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Explainable machine learning in materials science

Reference 27

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This paper cites Recent advances and applications of machine learning in solid-state materials science.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Recent advances and applications of machine learning in solid-state materials science

Reference 28

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This paper cites Confidence intervals for random forests: the jackknife and the infinitesimal jackknife.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Confidence intervals for random forests: the jackknife and the infinitesimal jackknife

Reference 29

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This paper cites On calibration of modern neural networks.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals On calibration of modern neural networks

Reference 30

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This paper cites FFMDFPA: A FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and Application Programming Interfaces.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals FFMDFPA: A FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and Application Programming Interfaces

Reference 31

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This paper cites Data-Driven Materials Science: Status, Challenges, and Perspectives.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Data-Driven Materials Science: Status, Challenges, and Perspectives

Reference 32

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This paper cites Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Reference 33

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Unresolved cited work

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This paper cites Practical Bayesian Optimization of Machine Learning Algorithms.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Practical Bayesian Optimization of Machine Learning Algorithms

Reference 35

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

Reference 36

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Gaussian process regression for materials and molecules

Reference 37

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Manifold Gaussian Processes for regression

Reference 38

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Deep Kernel Learning

Reference 39

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Stochastic variational deep kernel learning

Reference 40

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Deep Kernel learning for reaction outcome prediction and optimization

Reference 41

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Deep kernel learning improves molecular fingerprint prediction from tandem mass spectra

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Deep kernel methods learn better: from cards to process optimization

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals The promises and pitfalls of deep kernel learning

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Bayesian Methods for Neural Networks and Related Models

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Bayesian approach for neural networks—review and case studies

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Conference Paper

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 48

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 49

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Monte Carlo Sampling Methods Using Markov Chains and Their Applications

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals A Conceptual Introduction to Hamiltonian Monte Carlo

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Yes, but Did It Work?: Evaluating Variational Inference

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo

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This paper cites What uncertainties do we need in Bayesian deep learning for computer vision?.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals What uncertainties do we need in Bayesian deep learning for computer vision?

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Weight Uncertainty in Neural Network

Reference 56

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Laplace Redux - Effortless Bayesian Deep Learning

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Bayesian learning via stochastic gradient langevin dynamics

Reference 59

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals On the expressiveness of approximate inference in Bayesian neural networks

Reference 60

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Variational Inference: A Review for Statisticians

Reference 61

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Good Initializations of Variational Bayes for Deep Models

Reference 62

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Do Bayesian Neural Networks Need To Be Fully Stochastic?

Reference 63

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Variational Bayesian Last Layers

Reference 64

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Averaging Weights Leads to Wider Optima and Better Generalization

Reference 65

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Reference 66

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals How to evaluate uncertainty estimates in machine learning for regression?

Reference 67

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals RDKit: Open-source cheminformatics: https://www.rdkit.org

Reference 68

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals A general-purpose machine learning framework for predicting properties of inorganic materials

Reference 69

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals FreeSolv: a database of experimental and calculated hydration free energies, with input files

Reference 70

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals ESOL: Estimating Aqueous Solubility Directly from Molecular Structure

Reference 71

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

Reference 72

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals An open experimental database for exploring inorganic materials

Reference 73

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Reference 74

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Predicting the Band Gaps of Inorganic Solids by Machine Learning

Reference 75

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data

Reference 76

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals MoleculeNet: A Benchmark for Molecular Machine Learning

Reference 77

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals Inference from Iterative Simulation Using Multiple Sequences

Reference 78

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals General Methods for Monitoring Convergence of Iterative Simulations

Reference 79

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Active and transfer learning with partially Bayesian neural networks for materials and chemicals URL: https://www.sciencedirect

Reference 4928

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