MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving

Autonomous driving requires operation in different behavioral modes ranging from lane following and intersection crossing to turning and stopping. However, most existing deep learning approaches to autonomous driving do not consider the behavioral mode in the training strategy. This paper describes a technique for learning multiple distinct behavioral modes in a single deep neural network through the use of multi-modal multi-task learning. We study the effectiveness of this approach, denoted MultiNet, using self-driving model cars for driving in unstructured environments such as sidewalks and unpaved roads. Using labeled data from over one hundred hours of driving our fleet of 1/10th scale model cars, we trained different neural networks to predict the steering angle and driving speed of the vehicle in different behavioral modes. We show that in each case, MultiNet networks outperform networks trained on individual modes while using a fraction of the total number of parameters.Comment: Published in IEEE WACV 201

A Unified System for Molecular Property Predictions: Oloren ChemEngine and its Applications

Molecular property predictors form the core of any AI-enabled drug discovery strategy. In recent years, there has been significant research in this area, resulting in the development of powerful predictors and representations. However, these diverse predictors have different software interfaces, dependencies, and levels of documentation. Due to lack of a unified API for molecular property prediction, an AI-enabled drug discovery endeavor often necessitates a tangled web of scripts, notebooks, and configuration. This makes it is needlessly difficult to share, distribute, and manage predictors, to ensemble predictors together, and to provide universal AI explainability tools. To this end, we present Oloren ChemEngine (OCE), an open-source Python library with a unified API for molecular property predictors with simplified model management and reproducibility. Using OCE, we create models which achieve superior performance on ADME/Tox prediction tasks by ensembling and integrating many different molecular property prediction methods. We include model-agnostic uncertainty quantification using calibrated confidence intervals and probabilities as well as interpretability using counterfactual methods