  Raven's Progressive Matrices are a benchmark originally designed to test the
cognitive abilities of humans. It has recently been adapted to test relational
reasoning in machine learning systems. For this purpose the so-called
Procedurally Generated Matrices dataset was set up, which is so far one of the
most difficult relational reasoning benchmarks. Here we show that deep neural
networks are capable of solving this benchmark, reaching an accuracy of 98.0
percent over the previous state-of-the-art of 62.6 percent by combining Wild
Relation Networks with Multi-Layer Relation Networks and introducing Magnitude
Encoding, an encoding scheme designed for late fusion architectures.
