115 citations · 250 across the 8 of their papers we have counts for
9 papers
Explanatory Masks for Neural Network Interpretability
Lawrence Phillips, Garrett Goh, Nathan Hodas
Neural network interpretability is a vital component for applications across a wide variety of domains. In such cases it is often useful to analyze a network which has already been…
Multiple-objective Reinforcement Learning for Inverse Design and Identification
Haoran Wei, Mariefel Olarte, Garrett B. Goh
The aim of the inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are con…
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
Khushmeen Sakloth, Wesley Beckner, Jim Pfaendtner +1
Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successfu…
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
Garrett B. Goh, Khushmeen Sakloth, Charles Siegel +2
Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. H…
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…
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…
Deep Learning for Computational Chemistry
Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later,…