115 citations · 250 across the 8 of their papers we have counts for
4 papers · 1 filter
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…