Robust Deep Learning Projects

Resilient Dynamic Autoencoders for Modeling and Predicting Earthquake Threats

Large earthquakes generate strong ground motions and tsunamis that may lead to a significant number of casualties and cause severe impacts on social resilience in seismically active regions including the West Coast of the United States. Early warning systems have been developed to mitigate immediate threats by detecting first-arriving ground motions near an earthquake epicenter and forecasting the intensity and timing of strong destructive ground motions. To further improve the efficacy and accuracy of these systems, deep learning methods have strong potential, but it is crucial to significantly extend the forecast horizons of existing models.

Scalable Second-order Methods for Training, Designing, and Deploying Machine Learning Models

Scalable algorithms that can handle the large-scale nature of modern datasets are an integral part of many applications of machine learning (ML). Among these, efficient optimization algorithms, as the bread and butter of many ML methods, hold a special place. Optimization methods that use only first derivative information, i.e., first-order methods, are the most common tools used in training ML models. This is despite the fact that many of these methods come with inherent disadvantages such as slow convergence, poor communication, and the need for laborious hyper-parameter tuning.

Backdoor Detection via Eigenvalues, Hessians, Internal Behaviors, and Robust Statistics

Although Deep Neural Networks (DNNs) have achieved impressive performance in several applications, there are several by now well-known sensitivities that they exhibit. Perhaps the most prominent of these is sensitivity in various types of adversarial environments. As an example of this, recall that it is common in practice to outsource the training of a model (which is known as Machine Learning as a Service, MLaaS) or to use third-party pre-trained networks (and then perform fine-tuning or transfer learning).