The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

As one of the most comprehensive and objective ways to describe facial expressions, the
Facial Action Coding System (FACS) has recently received significant attention. Over the …

Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …

Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many domains …

3D semantic scene labeling is fundamental to agents operating in the real world. In
particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent …

We study the problem of designing models for machine learning tasks defined on sets. In
contrast to the traditional approach of operating on fixed dimensional vectors, we consider …

Many real-world analytics problems involve two significant challenges: prediction and
optimization. Because of the typically complex nature of each challenge, the standard …

State-of-the-art models for joint entity recognition and relation extraction strongly rely on
external natural language processing (NLP) tools such as POS (part-of-speech) taggers and …

Machine learning is one of the fastest growing areas of computer science, with far-reaching
applications. The aim of this textbook is to introduce machine learning, and the algorithmic …

Protein secondary structure (SS) prediction is important for studying protein structure and
function. When only the sequence (profile) information is used as input feature, currently the …