LEARNING TO MAKE ANALOGIES BY CONTRASTING ABSTRACT RELATIONAL STRUCTURE
Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data. We find that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model. The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains, a training method that uses only the input data to force models to learn about important abstract features. Using this technique we demonstrate capacities for complex, visual and symbolic analogy making and generalisation in even the simplest neural network architectures.
Cons
1.	It’s unclear why LABC produces lower scores than ‘normal’ training on ‘normal’ testing.
2.	The text says nothing I can find to explain why in Fig 5 the ‘entity’ vectors have all 0s except in one dimension, which seems to make the problem considerably easier.
3.	In a sense, there is no cross-domain adaptation required in the symbolic task: min is min, whether it operates on dimension k of the source vectors or dimension j of the target vectors. On the other hand, dimensions are processed independently in the model, as far as I can tell, so there’s no free transfer of learning min on dimension k to knowing min on dimension j. It would be good to comment on this issue.
4.	There seem to be obvious analogies (so to speak) to GANs, and it is very curious that this is not mentioned anywhere that I can see. This is particularly glaring in Sec. 5.3.
5.	The quantitative results are scattered throughout the prose; it would be challenging, but worthwhile, to gather them into an actual table.
Pros
6.	The basic idea (“We should aspire to select as negative examples those examples that are plausible considering the most abstract principles that describe the data”, p. 14) is very intuitive, common-sensical, bordering on obvious. But it is not at all obvious that the idea has as much power as is demonstrated in the experiments. The transfer to novel domain combinations, novel domains, and novel values of dimensions is impressive and surprising.
7.	The result that the proposed training, designed to promote generalization on analogy tasks, also seems to promote improved sensory processing is interesting. Whether it really instantiates the parallel connection argued for by the High-Level Perception view from psychology/philosophy is debatable, but that is itself an interesting connection that the authors should be praised for identifying.
8.	In general, the connection to the cognitive literature is creative and tantalizing and provides good scientific grounding for the work.
9.	The linking to the flexibility of word meanings in the final paragraph pushes the limit of the plausibility of connection to broader cognitive issues, but I’m inclined to indulge the authors for at least bringing up this important and relevant issue.