Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders | Paper Citation Record

Observation e79c1d7f-1d5d-46e3-8a6b-7141e4296c07 · outbound

This paper cites Bluesky’s ahead: A multi-facility collaboration for an a la carte software project for data acquisition and manage- ment.Synchrotron Radiation News, 32(3):19–22, 2019.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Bluesky’s ahead: A multi-facility collaboration for an a la carte software project for data acquisition and manage- ment.Synchrotron Radiation News, 32(3):19–22, 2019

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This paper cites Advancing discovery with artificial intelligence and machine learning at nsls-ii.Synchrotron Radiation News, 35(4):44–50, 2022.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Advancing discovery with artificial intelligence and machine learning at nsls-ii.Synchrotron Radiation News, 35(4):44–50, 2022

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This paper cites Learning metal microstructural heterogeneity through spatial mapping of diffraction latent space features.npj Computational Materials, 11(1):284, 2025.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Learning metal microstructural heterogeneity through spatial mapping of diffraction latent space features.npj Computational Materials, 11(1):284, 2025

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This paper cites Density-based clustering based on hierarchical density estimates.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Density-based clustering based on hierarchical density estimates

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This paper cites Emerging properties in self-supervised vision transformers.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Emerging properties in self-supervised vision transformers

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This paper cites Data augmentation with variational autoen- coders and manifold sampling.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Data augmentation with variational autoen- coders and manifold sampling

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This paper cites Zwart, Daniel B.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Zwart, Daniel B

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This paper cites A machine- learning-driven data labeling pipeline for scientific analysis in mlexchange.Applied Crystallog- raphy, 58(3), 2025.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders A machine- learning-driven data labeling pipeline for scientific analysis in mlexchange.Applied Crystallog- raphy, 58(3), 2025

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This paper cites Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data.Integrating Materials and Manufacturing Innovation, 10(2):231–244, 2021.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data.Integrating Materials and Manufacturing Innovation, 10(2):231–244, 2021

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders An image is worth 16x16 words: Transformers for image recognition at scale

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This paper cites Evolution of ionomer morphology from dispersion to film: an in situ x-ray study.Macromolecules, 52(20):7779–7785, 2019.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Evolution of ionomer morphology from dispersion to film: an in situ x-ray study.Macromolecules, 52(20):7779–7785, 2019

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This paper cites A saxs/waxs/gisaxs beamline with multilayer monochromator.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders A saxs/waxs/gisaxs beamline with multilayer monochromator

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This paper cites Burgess, Xavier Glorot, Matthew M.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Burgess, Xavier Glorot, Matthew M

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This paper cites Classifier-Free Diffusion Guidance.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Classifier-Free Diffusion Guidance

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This paper cites Ai-nerd: Elucidation of relaxation dynamics beyond equilibrium through ai-informed x-ray photon correlation spectroscopy.Nature Communications, 15(1):5945, 2024.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Ai-nerd: Elucidation of relaxation dynamics beyond equilibrium through ai-informed x-ray photon correlation spectroscopy.Nature Communications, 15(1):5945, 2024

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This paper cites Interactive visual study of multiple attributes learning model of x-ray scattering images.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Interactive visual study of multiple attributes learning model of x-ray scattering images

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Observation 279c12fb-b81e-4f06-a505-3520a6b489bc · outbound

This paper cites Exploring order pa- rameters and dynamic processes in disordered systems via variational autoencoders.Science Advances, 7(17):eabd5084, 2021.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Exploring order pa- rameters and dynamic processes in disordered systems via variational autoencoders.Science Advances, 7(17):eabd5084, 2021

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This paper cites Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder.Ma- terials & Design, 202:109544, 2021.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder.Ma- terials & Design, 202:109544, 2021

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This paper cites Auto-encoding variational bayes.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Auto-encoding variational bayes

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This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

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This paper cites New insights into perfluorinated sulfonic-acid ionomers.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders New insights into perfluorinated sulfonic-acid ionomers

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This paper cites Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration

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This paper cites A deep- learning technique for phase identification in multiphase inorganic compounds using synthetic xrd powder patterns.Nature communications, 11(1):86, 2020.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders A deep- learning technique for phase identification in multiphase inorganic compounds using synthetic xrd powder patterns.Nature communications, 11(1):86, 2020

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Flow Matching for Generative Modeling

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Swin transformer: Hierarchical vision transformer using shifted windows

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Donut: physics-aware machine learning for real-time x-ray nanodiffraction analysis.npj Com- putational Materials, 11(1):380, 2025

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Principal components analysis (pca).Comput- ers & Geosciences, 19(3):303–342, 1993

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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This paper cites Nersc perlmutter archi- tecture.https://docs.nersc.gov/systems/perlmutter/architecture/, 2024.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Nersc perlmutter archi- tecture.https://docs.nersc.gov/systems/perlmutter/architecture/, 2024

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders A kriging-based approach to autonomous experimentation with applications to x-ray scattering.Scientific reports, 9(1):11809, 2019

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders DINOv2: Learning Robust Visual Features without Supervision

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This paper cites Ai@ als workshop report: machine learning needs at the advanced light source, 2024.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Ai@ als workshop report: machine learning needs at the advanced light source, 2024

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments.Synchrotron Radiation, 30(6):1064– 1075, 2023

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders On-the-fly segmen- tation approaches for x-ray diffraction datasets for metallic glasses.MRS Communications, 7(3):613–620, 2017

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Auto- mated classification of big x-ray diffraction data using deep learning models.npj Computational Materials, 9(1):214, 2023

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Featureforest: the power of foundation models, the usability of random forests.npj Imaging, 3(1):32, 2025

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders DINOv3

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Tracking perovskite crystallization via deep learning-based feature detection on 2d x-ray scattering data

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Can unsupervised machine learning boost the on-site analysis of in situ synchrotron diffraction data?Scripta materialia, 226:115238, 2023

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Au- tosas: A new human-aside-the-loop paradigm for automated sas fitting for high throughput and autonomous experimentation.APL Machine Learning, 3(3), 2025

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Adaptively driven x-ray diffraction guided by machine learning for au- tonomous phase identification.npj Computational Materials, 9(1):31, 2023

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders.npj Computational Materials, 10(1):183, 2024

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Attention is all you need.Advances in neural information processing systems, 30, 2017

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Accelerating the machine learning lifecycle with mlflow.IEEE Data Eng

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Chemnav: An interactive visual tool to navigate in the latent space for chemical molecules discovery.Visual Informatics, 8(4):60–70, 2024

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders To- wards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.The Innovation, 5(1), 2024

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Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Mlexchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies

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This paper cites Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop Annotations.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop Annotations

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This paper cites A machine learning model for textured x-ray scattering and diffraction image denoising.npj Computational Materials, 9(1):58, 2023.

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders A machine learning model for textured x-ray scattering and diffraction image denoising.npj Computational Materials, 9(1):58, 2023

Reference 50

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