Measuring abstract reasoning in neural networks

David Barrett, Felix Hill, Adam Santoro, Ari Morcos, Timothy Lillicrap
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:511-520, 2018.

Abstract

Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation ’regimes’ in which the training data and test questions differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model’s ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-barrett18a, title = {Measuring abstract reasoning in neural networks}, author = {Barrett, David and Hill, Felix and Santoro, Adam and Morcos, Ari and Lillicrap, Timothy}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {511--520}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/barrett18a/barrett18a.pdf}, url = {https://proceedings.mlr.press/v80/barrett18a.html}, abstract = {Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation ’regimes’ in which the training data and test questions differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model’s ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction.} }
Endnote
%0 Conference Paper %T Measuring abstract reasoning in neural networks %A David Barrett %A Felix Hill %A Adam Santoro %A Ari Morcos %A Timothy Lillicrap %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-barrett18a %I PMLR %P 511--520 %U https://proceedings.mlr.press/v80/barrett18a.html %V 80 %X Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation ’regimes’ in which the training data and test questions differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model’s ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction.
APA
Barrett, D., Hill, F., Santoro, A., Morcos, A. & Lillicrap, T.. (2018). Measuring abstract reasoning in neural networks. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:511-520 Available from https://proceedings.mlr.press/v80/barrett18a.html.

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