Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

Deep generative models allow for photorealistic image synthesis at high resolutions. But for
many applications, this is not enough: content creation also needs to be controllable. While …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the
task of visual generative modeling. The network employs a bipartite structure that enables …

Recent studies have shown that StyleGANs provide promising prior models for downstream
tasks on image synthesis and editing. However, since the latent codes of StyleGANs are …

Advances in unsupervised learning of object-representations have culminated in the
development of a broad range of methods for unsupervised object segmentation and …

We present ObSuRF, a method which turns a single image of a scene into a 3D model
represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to …

Scalable sensor simulation is an important yet challenging open problem for safety-critical
domains such as self-driving. Current works in image simulation either fail to be …

Relations are basic building blocks of human cognition. Classic and recent work suggests
that many relations are early developing, and quickly perceived. Machine models that aspire …