6 citations · 10 across the 5 of their papers we have counts for
5 papers
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities
Arpan Biswas, Sai Mani Prudhvi Valleti, Rama Vasudevan +2
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams…
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas +5
Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-…
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments
Arpan Biswas, Yongtao Liu, Nicole Creange +6
Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements…
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis
Arpan Biswas, Maxim Ziatdinov, Sergei V. Kalinin
Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range o…
Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach
Arpan Biswas, Rama Vasudevan, Maxim Ziatdinov +1
Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences du…