Today, there are two major paradigms for vision-based autonomous driving systems:
mediated perception approaches that parse an entire scene to make a driving decision, and …

While there has been remarkable progress in the performance of visual recognition algorithms,
the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training …

Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of …

Automated computer-aided detection (CADe) has been an important tool in clinical practice
and research. State-of-the-art methods often show high sensitivities at the cost of high false-…

Automated Lymph Node (LN) detection is an important clinical diagnostic task but very
challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and …

Developments in deep generative models have allowed for tractable learning of high-dimensional
data distributions. While the employed learning procedures typically assume that …

Automated classification of human anatomy is an important prerequisite for many computer-aided
diagnosis systems. The spatial complexity and variability of anatomy throughout the …

Despite tremendous progress in computer vision, there has not been an attempt to apply
machine learning on very large-scale medical image databases. We present an interleaved text/…

The success of generative modeling in continuous domains has led to a surge of interest in
generating discrete data such as molecules, source code, and graphs. However, …

Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering
for physical design. Distinguished by relational geometry, parametric CAD models begin …