Search Results for author: Rama Vasudevan

Found 4 papers, 1 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.

Edge-computing

Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach

1 code implementation30 Jun 2022 Arpan Biswas, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin

Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and regression of complex experimental data.

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

Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution Scanning Transmission Electron Microscopy data

no code implementations11 Feb 2020 Maxim Ziatdinov, Chris Nelson, Xiaohang Zhang, Rama Vasudevan, Eugene Eliseev, Anna N. Morozovska, Ichiro Takeuchi, Sergei V. Kalinin

Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.

Materials Science

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