CIS

ABSTRACT Traditional convolutional networks exhibit unprecedented robustness to intraclass nuisances when trained on big data. Generalization with respect to geometric transformations has been achieved via expensive data augmentation. It has been shown recently that data augmentation can be avoided if networks are structured such that feature representations are transformed the same way as the input, a desirable property called equivariance. In […]

Abstract Sensing and actuation systems are entrusted with increasing intelligence to perceive the environment and react to it. Their reliability often relies on the trustworthiness of sensors. As process automation and robotics keep evolving, sensing methods such as pressure/temperature/motion sensing are extensively used in conventional systems and rapidly emerging applications. This talk aims to investigate […]

Abstract: The relevant limit for machine learning is not N → infinity but instead N → 0. The human visual system is proof that it is possible to learn categories with extremely few samples. This talk will discuss steps towards building such systems and it is structured in three parts. The first part will discuss […]

Abstract: Recently, the transfer learning paradigm has seen a surge of interest due to its impressive capabilities in vision and language. Models are pretrained on ever-growing datasets with enormous parameter counts, trending towards being monolithic and opaque. How can we understand the underlying process? This talk will provide, to some degree, insight on how data […]

ABSTRACT: Machine learning systems, in virtually every deployed system, encounter data from a qualitatively different distribution than what they were trained upon.  Effectively dealing with this problem, known as domain shift, is thus perhaps the key challenge in deploying machine learning methods in practice.  In this talk, I will motivate some of these challenges in […]

ABSTRACT:BIO Machine learning has become an indispensable aid to researchers developing the next generation of novel therapeutics. In this talk, I will discuss how some of the most important problems  in virtual screening for new potential drug molecules can be cast as black-box optimization problems, where the goal is to find molecules maximizing some desired property […]

ABSTRACT: The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in practice and are not designed to be verifiable. In this talk, I will present several of our recent efforts that combine ML with formal methods […]