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Found 5 papers, 3 papers with code
Towards Classification of Legal Pharmaceutical Text using GAN-BERT
Pharmaceutical text classification is an important area of research for commercial and research institutions working in the pharmaceutical domain.
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Microstructural materials design is one of the most important applications of inverse modeling in materials science.
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run.
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell Screening
In this work, we present an ensemble deep neural network architecture, called SINet, which harnesses both the SMILES and InChI molecular representations to predict HOMO values and leverage the potential of transfer learning from a sizeable DFT-computed dataset- Harvard CEP to build more robust predictive models for relatively smaller HOPV datasets.
CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations
SMILES is a linear representation of chemical structures which encodes the connection table, and the stereochemistry of a molecule as a line of text with a grammar structure denoting atoms, bonds, rings and chains, and this information can be used to predict chemical properties.
