115 citations · 331 across the 10 of their papers we have counts for
6 papers · 1 filter
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +1
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small…
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Garrett B. Goh, Nathan O. Hodas, Charles Siegel +1
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is…
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +2
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representat…
What does fault tolerant Deep Learning need from MPI?
Vinay Amatya, Abhinav Vishnu, Charles Siegel +1
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed…
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +2
In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's In…
User-transparent Distributed TensorFlow
Abhinav Vishnu, Joseph Manzano, Charles Siegel +1
Deep Learning (DL) algorithms have become the {\em de facto} choice for data analysis. Several DL implementations -- primarily limited to a single compute node -- such as Caffe, Te…