A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

Machine learning has long since become a keystone technology, accelerating science and
applications in a broad range of domains. Consequently, the notion of applying learning …

Answering visual queries is a complex task that requires both visual processing and
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Learning models that gracefully handle distribution shifts is central to research on domain
generalization, robust optimization, and fairness. A promising formulation is domain …

Generalization remains a central challenge in machine learning. In this work, we propose
Learning from Teaching (LoT), a novel regularization technique for deep neural networks to …

State-of-the-art machine learning methods exhibit limited compositional generalization. At
the same time, there is a lack of realistic benchmarks that comprehensively measure this …

We introduce DeepProbLog, a probabilistic logic programming language that incorporates
deep learning by means of neural predicates. We show how existing inference and learning …

Despite a multitude of empirical studies, little consensus exists on whether neural networks
are able to generalise compositionally, a controversy that, in part, stems from a lack of …

Deep learning for AI Page 1 58 COMMUNICATIONS OF THE ACM | JULY 2021 | VOL. 64 |
NO. 7 turing lecture RESEARCH ON ARTIFICIAL neural networks was motivated by the …