Deep generative models have been an upsurge in the deep learning community since they
were proposed. These models are designed for generating new synthetic data including …

The rise of machine learning has fueled the discovery of new materials and, especially,
metamaterials—truss lattices being their most prominent class. While their tailorable …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

The process of recognizing and distinguishing between real content and content generated
by deep learning algorithms, often referred to as deepfakes, is known as deepfake detection …

Recent advances in learning reusable motion priors have demonstrated their effectiveness
in generating naturalistic behaviors. In this paper, we propose a new learning framework in …

Cognitive diagnosis is an important task in intelligence education, which aims at measuring
students' proficiency in specific knowledge concepts. Given a fully labeled exercise-concept …

A prominent goal of representation learning research is to achieve representations which
are factorized in a useful manner with respect to the ground truth factors of variation. The …

Abstract Representation disentanglement may help AI fundamentally understand the real
world and thus benefit both discrimination and generation tasks. It currently has at least …

We propose a novel training method that integrates rules into deep learning, in a way the
strengths of the rules are controllable at inference. Deep Neural Networks with Controllable …