6 citations · 8 across the 3 of their papers we have counts for
3 papers
Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models
Onur Kara, Arijit Sehanobish, Hector H Corzo
Transformers are state-of-the-art deep learning models that are composed of stacked attention and point-wise, fully connected layers designed for handling sequential data. Transfor…
Learning Full Configuration Interaction Electron Correlations with Deep Learning
Hector H. Corzo, Arijit Sehanobish, Onur Kara
In this report, we present a deep learning framework termed the Electron Correlation Potential Neural Network (eCPNN) that can learn succinct and compact potential functions. These…
Learning Potentials of Quantum Systems using Deep Neural Networks
Arijit Sehanobish, Hector H. Corzo, Onur Kara +1
Attempts to apply Neural Networks (NN) to a wide range of research problems have been ubiquitous and plentiful in recent literature. Particularly, the use of deep NNs for understan…