Search Results for author: Yongtao Liu

Found 7 papers, 4 papers with code

Microscopy is All You Need

no code implementations12 Oct 2022 Sergei V. Kalinin, Rama Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim Ziatdinov

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods.


Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning

no code implementations30 May 2022 Maxim Ziatdinov, Yongtao Liu, Kyle Kelley, Rama Vasudevan, Sergei V. Kalinin

Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the forefront of attention of the scientific community.

Active Learning Bayesian Inference +3

Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning

1 code implementation18 Mar 2022 Maxim Ziatdinov, Yongtao Liu, Sergei V. Kalinin

Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation.

Active Learning

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

1 code implementation24 May 2021 Maxim Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, Sergei V. Kalinin

The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules.

Representation Learning Semi-Supervised Image Classification

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

1 code implementation20 Apr 2021 Yongtao Liu, Rama K. Vasudevan, Kyle Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim Ziatdinov

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables.

Denoising Dimensionality Reduction

Heterochromatic nonlinear optical responses in upconversion nanoparticles for point spread function engineering

no code implementations12 Dec 2020 Chaohao Chen, Baolei Liu, Yongtao Liu, Jiayan Liao, Xuchen Shan, Fan Wang, Dayong Jin

Point spread function (PSF) engineering of the emitter can code higher spatial frequency information of an image to break diffraction limit but suffer from the complexed optical systems.


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