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

Observation b6898fe7-b4ad-4606-8cee-c6c7cfd06842 · outbound

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

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:44.163147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd8cd0b0-0ac4-45eb-a438-ca0de7e66b91 · outbound

This paper cites Active Learning Literature Survey.

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

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:44.143978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99b32c63-5e4f-4143-b248-38077b1405fb · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:38.617346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03261daf-721c-4c79-a024-5b217ce444c5 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:38.706370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be34f019-97f5-4ab5-b1d5-f538123c2adc · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:38.715775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 939dd56d-796a-436e-8086-74d342f4047c · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:43.613868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 63ee9dd9-f1e4-4677-b0d8-ad7038e81a51 · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:43.514741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6611d058-7567-4ed6-b6a2-1e0bb33c8c83 · outbound

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

Resolution: verified exact; doi, observed 2026-08-10T22:43:41.347828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0602aaee-f68c-4369-ae08-f93c7bca4db3 · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:43.424620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4cba933b-4fb2-45ed-9134-48093d16e9fe · outbound

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

Resolution: verified exact; doi, observed 2026-08-10T22:43:41.306529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 02ec302b-0e5d-475b-87c3-792fbd96f47a · outbound

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

Resolution: malformed identifier; no resolver link, observed 2026-08-10T22:43:38.767770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b738c61f-ffd2-4c0c-9d23-f1ec3653a16e · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:38.772973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5edf4a0-8435-4bee-bd25-d53d64bdddf8 · outbound

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

Resolution: verified exact; doi, observed 2026-08-10T22:43:41.271594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ac669bb-81f8-4aba-aa20-587a4639b669 · outbound

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

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:43:44.128757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3a719f7-8653-4c97-8b6e-158e80c3371b · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:44.106249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ce07d8a-7e49-4bd0-9dd5-73b4d96d6d31 · outbound

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

Resolution: verified exact; doi, observed 2026-08-10T22:43:41.211625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d42504da-124f-4cdd-96c8-510be99d10f6 · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:43.254421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 76d40042-c2ea-420f-b2e7-c7d0dd6146a9 · outbound

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

Resolution: verified exact; doi, observed 2026-08-10T22:43:41.138909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e47affd9-3c3d-4648-a1ab-7e72859dc03c · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:43.161504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64805881-b414-4d52-b0a1-6581302cbbf0 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.078090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33b26097-5dfa-44d7-bd2c-095e38722795 · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:43.081497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bdaf74bc-7844-4c8f-bde3-85d958ffcb1b · outbound

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

Resolution: malformed identifier; no resolver link, observed 2026-08-10T22:43:39.101707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fcf9e19-f053-4d8d-9fc4-dbbde70e9af3 · outbound

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

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:43:42.975376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b0dcb39-0ff5-45f2-afe7-607c9b029586 · outbound

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

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:43:44.079328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2e8bd1c6-bd9c-4e13-b093-8110c56d25a8 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.121774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1f26dbf-2693-4f50-9c90-c2470bd85661 · outbound

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

Resolution: verified exact; doi, observed 2026-08-10T22:43:40.984107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a0d340f-b958-42f2-b775-406fab053cfb · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.148468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aaa1e61-fd2c-48c4-94d2-7bafe3d2cdbe · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:44.060709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd2cd84f-3fcd-4ab0-bb5e-5a571bd4fe96 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:44.042494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2c260783-7b19-4ef1-9af8-0b00f83f42ba · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:42.855841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e2bf30c-cd03-4974-879a-5abdf93f340d · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.474542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d24107c-a838-4d90-b32a-05f868d0b148 · outbound

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:42.764245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4690188d-cb32-4c73-977b-696560d00c49 · outbound

This paper cites an unresolved cited work.

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

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.537207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34c38d4e-35e5-40dd-882a-b6369ddf98bd · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:44.028759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a7c7df3-71d9-4e4e-953e-e9de83c0795a · outbound

This paper cites Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences.

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.582003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e828cea-62b7-4428-ac57-f26a45e67f6c · outbound

This paper cites Gaussian process regression for materials and molecules.

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

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:44.015125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f2d9d968-707a-4a6f-a596-01f799217281 · outbound

This paper cites Manifold Gaussian Processes for regression.

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

Reference 38

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T22:43:42.663910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 15e059e5-4601-44e0-a64a-b696d2632e5a · outbound

This paper cites Deep Kernel Learning.

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

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.990284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c3f0b2b5-3f6e-42e4-988a-20ddcc4edf7c · outbound

This paper cites Stochastic variational deep kernel learning.

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

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.972644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 687ad010-c3c6-4291-99ac-cefffead5e41 · outbound

This paper cites Deep Kernel learning for reaction outcome prediction and optimization.

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

Reference 41

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:43:42.577185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f7a193c6-2587-4f87-9044-b37905d1e393 · outbound

This paper cites Deep kernel learning improves molecular fingerprint prediction from tandem mass spectra.

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

Reference 42

Resolution: verified exact; doi, observed 2026-08-10T22:43:40.859728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5fcdfc0c-b677-4d7d-990e-b7a34fd4ce78 · outbound

This paper cites Deep kernel methods learn better: from cards to process optimization.

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

Reference 43

Resolution: verified exact; doi, observed 2026-08-10T22:43:40.718352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3cd673ff-09eb-42c3-900b-350bd387e2b2 · outbound

This paper cites The promises and pitfalls of deep kernel learning.

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

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.958774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ea66b15d-59e5-419f-be41-9e2c72e7a1eb · outbound

This paper cites Bayesian Methods for Neural Networks and Related Models.

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

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.756050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ea8f5e6-0213-4b43-a185-a7d81d856b22 · outbound

This paper cites Bayesian approach for neural networks—review and case studies.

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

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.946280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 67ecb733-2257-4c97-bd1c-f64a880a2455 · outbound

This paper cites Conference Paper.

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

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.889225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 648e6135-9f53-4c01-b0c3-42bad0e33099 · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.932714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98df5d5e-ca5c-4ce7-99ed-1caac9e99ddf · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

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

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.919999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 289e14fe-d566-4ea2-8b82-cc293e34730d · outbound

This paper cites Monte Carlo Sampling Methods Using Markov Chains and Their Applications.

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

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.905573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7162ea11-04b6-48e6-a630-53a995c5a70e · outbound

This paper cites A Conceptual Introduction to Hamiltonian Monte Carlo.

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

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:39.950641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54f87e9d-b3d2-4ffc-a541-296671ece82e · outbound

This paper cites Yes, but Did It Work?: Evaluating Variational Inference.

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

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.888194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1d41fe69-f8ab-455e-82ad-05581fa8833b · outbound

This paper cites The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.

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

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.871407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a814c1d6-acb2-4332-bb0b-9d33d70ce3b3 · outbound

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?

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.855960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 118874ac-c17d-4f5a-ab4e-a5fbe93b91c0 · outbound

This paper cites Weight Uncertainty in Neural Network.

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

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.840463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0010801f-e38c-49d7-8d5e-aa8654169719 · outbound

This paper cites Laplace Redux - Effortless Bayesian Deep Learning.

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

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.827325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fbc2bc76-f066-441c-b538-890befa2ccb3 · outbound

This paper cites an unresolved cited work.

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

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:43:43.810006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1beec6da-d028-4847-874c-a55fecc7bbf5 · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics.

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

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.795088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1dc3616b-1245-40f5-af0d-56823cecf884 · outbound

This paper cites On the expressiveness of approximate inference in Bayesian neural networks.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.777259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7b762c80-a05b-40ee-bdc7-d801be70c65e · outbound

This paper cites Variational Inference: A Review for Statisticians.

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

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.194441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e28f8ea-b2b7-4e28-a0ac-80087be22889 · outbound

This paper cites Good Initializations of Variational Bayes for Deep Models.

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

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.757062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0ff43f7-8a1a-43fd-9fbd-4e2653adb477 · outbound

This paper cites Do Bayesian Neural Networks Need To Be Fully Stochastic?.

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

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.252286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d09fe83d-15ae-4b9b-b0c4-13f76bcdf59e · outbound

This paper cites Variational Bayesian Last Layers.

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

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.256394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 350da848-b57f-47c7-a6bf-fe3010bd34d8 · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

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

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.264911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5287ce85-0be7-4376-81b9-7dc9cf629440 · outbound

This paper cites Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures.

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

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.270378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fef54807-6764-45d8-9615-a353f4f6d34f · outbound

This paper cites How to evaluate uncertainty estimates in machine learning for regression?.

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.276451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ee2070-a2ad-4d89-aa36-efdd9176f42b · outbound

This paper cites RDKit: Open-source cheminformatics: https://www.rdkit.org.

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

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.724824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 88bf4ed9-d18f-4e52-9add-e4d6b0c6b7e4 · outbound

This paper cites A general-purpose machine learning framework for predicting properties of inorganic materials.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:43:43.708345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e34bf17d-5fd3-486f-9d2e-2c785631d228 · outbound

This paper cites FreeSolv: a database of experimental and calculated hydration free energies, with input files.

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

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:42.039026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a6f815a-ba54-474c-8127-0af53628ddd4 · outbound

This paper cites ESOL: Estimating Aqueous Solubility Directly from Molecular Structure.

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

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.418929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bddc98d-8d70-41b8-b4f2-7fed2dec01f3 · outbound

This paper cites Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters.

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.465332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56e8bceb-dad1-4e9b-bef0-6f75fee8f83a · outbound

This paper cites An open experimental database for exploring inorganic materials.

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

Reference 73

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:43:41.894857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 29a36cff-7605-4a93-b371-9da2b5180961 · outbound

This paper cites Commentary: The Materials Project: A materials genome approach to accelerating materials innovation.

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.524547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ae9bc81-f108-4ebf-8e60-9e47587b8823 · outbound

This paper cites Predicting the Band Gaps of Inorganic Solids by Machine Learning.

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

Resolution: malformed identifier; no resolver link, observed 2026-08-10T22:43:40.534451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ab1e4ad-2283-4be3-8cdd-e9e73546c751 · outbound

This paper cites Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data.

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.540576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e0cc70b-6272-4982-95cc-05566304bdd4 · outbound

This paper cites MoleculeNet: A Benchmark for Molecular Machine Learning.

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

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.547698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdbe83b9-ec2e-4cf0-95a5-682f8a4ca60b · outbound

This paper cites Inference from Iterative Simulation Using Multiple Sequences.

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

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:40.558138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 475f3311-7367-45b5-bd9d-c30a6bd84e95 · outbound

This paper cites General Methods for Monitoring Convergence of Iterative Simulations.

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

Reference 79

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T22:43:41.584687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ff0f1d25-9e59-404b-bf52-739f2d81825c · outbound

This paper cites URL: https://www.sciencedirect.

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

Reference 4928

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:38.898291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 640fa424-a7e1-4e72-83fe-f277005f2b3e · outbound

This paper cites an unresolved cited work.

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

Reference 5497

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:43:43.740277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers