Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Program synthesis or code generation aims to generate a program that satisfies a problem
specification. Recent approaches using large-scale pretrained language models (LMs) have …

Abstraction is at the heart of sketching due to the simple and minimal nature of line
drawings. Abstraction entails identifying the essential visual properties of an object or scene …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

We present a system for inductive program synthesis called DreamCoder, which inputs a
corpus of synthesis problems each specified by one or a few examples, and automatically …

We introduce a differentiable rasterizer that bridges the vector graphics and raster image
domains, enabling powerful raster-based loss functions, optimization procedures, and …

Conceptual abstraction and analogy‐making are key abilities underlying humans' abilities to
learn, reason, and robustly adapt their knowledge to new domains. Despite a long history of …

This paper presents an unpaired method for creating line drawings from photographs.
Current methods often rely on high quality paired datasets to generate line drawings …

Training models to high-end performance requires availability of large labeled datasets,
which are expensive to get. The goal of our work is to automatically synthesize labeled …