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Machine Learning-Powered Experimental Sciences | ❤️ Open Source.
gpax. Gaussian Processes for Experimental Sciences
241SciLink. LLM-powered agents for scientific research automation
89NeuroBayes. Fully and Partially Bayesian Neural Nets
85GPim. Gaussian processes and Bayesian optimization for images and hyperspectral data
57pyroVED. Invariant representation learning from imaging and spectral data
54AugmentedGaussianProcess. Gaussian process augmented with a probabilistic model of expected system's behavior
15Notebooks-for-papers. Jupyter notebooks describing data analysis procedures for my published/submitted papers
14atomai. Deep and machine learning for atomic-scale and mesoscale data
13hypoAL. Jupyter Notebook
5ActiveChannelLearning. Automated selection of channels with best predictive capacity in multimodal imaging and spectroscopy experiments
4APS2020Tutorial. Tutorial on image analysis with deep / machine learning for APS-2020 meeting in Denver
3notebooks_for_medium. Jupyter notebooks for our Medium articles
2AISTEM_WORKSHOP_2020. Materials for "AI for Atoms: How to Machine Learn STEM" Workshop
2GP. This repo will now be developed and maintained https://github.com/ziatdinovmax/GPim
2dualVAE. Jupyter Notebook
2AtomicImageSimulator. Jupyter Notebook
2MHP_stability. Jupyter Notebook
1im2spec. Jupyter Notebook
1AIML-tutorials. Repository for containing the tutorial series for the AI/ML working group
1MRS2021. Notebooks for MRS2021 tutorial
1CSSAS-DML. Jupyter Notebook
1Semi-Supervised-VAE-nanoparticles. Semi-supervised VAE for data with the rotational disorder
1jtrVAE.
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