This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

The modern computer graphics pipeline can synthesize images at remarkable visual quality;
however, it requires well-defined, high-quality 3D content as input. In this work, we explore …

Modern rendering systems are confronted with a dauntingly large and growing set of
requirements: in their pursuit of realism, physically based techniques must increasingly …

We present DiffTaichi, a new differentiable programming language tailored for building high-
performance differentiable physical simulators. Based on an imperative programming …

DR. JIT is a new just-in-time compiler for physically based rendering and its derivative. DR.
JIT expedites research on these topics in two ways: first, it traces high-level simulation code …

High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …

In this article, we perform a review of the state-of-the-art of hybrid machine learning in
medical imaging. We start with a short summary of the general developments of the past in …

We present a new algorithm to automatically schedule Halide programs for high-
performance image processing and deep learning. We significantly improve upon the …

Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their
spatial resolution; smaller apertures, which limits their light gathering ability; and smaller …