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A simple neural network module for relational reasoning

Adam Santoro, David Raposo, David G. T. Barrett

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Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap

2017 · arXiv

DatabasesMachine learningPsychology
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From the abstract

Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We tested RN-augmented networks on three tasks: visual question answering using a challenging dataset called CLEVR, on which we achieve state-of-the-art, super-human performance; text-based question answering using the bAbI suite of tasks; and complex reasoning about dynamic physical systems. Then, using a curated dataset called Sort-of-CLEVR we show that powerful convolutional networks do not have a general capacity to solve relational questions, but can gain this capacity when augmented with RNs. Our work shows how a deep learning architecture equipped with an RN module can implicitly discover and learn to reason about entities and their relations.

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Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels

4 selected excerpts · 4 total mentions

cute_boi
Neutral mention · rule
No need stupid moving texts. CS231n: Convolutional Neural Networks for Visual Recognition - https://cs231n.github.io/ The Unreasonable Effectiveness of Recurrent Neural Networks - https://karpathy.github.io/2015/05/21/rnn-effectiveness/ Understanding LSTM Networks - https://colah.github.io/posts/2015-08-Understanding-LSTMs/ ImageNet Classification with Deep Convolutional Neural Networks - https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... Deep Residual Learning for Image Recognition - https://arxiv.org/abs/1512.03385 Multi-Scale Context Aggregation by Dilated Convolutions - https://arxiv.org/abs/1511.07122 Identity Mappings in Deep Residual Networks -…
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https://arxiv.org/abs/1603.05027 Recurrent Neural Network Regularization - https://arxiv.org/abs/1409.2329 Deep Speech 2: End-to-End Speech Recognition in English and Mandarin - https://arxiv.org/abs/1512.02595 Order Matters: Sequence to Sequence for Sets - https://arxiv.org/abs/1511.06391 Neural Machine Translation by Jointly Learning to Align and Translate - https://arxiv.org/abs/1409.0473 Pointer Networks - https://arxiv.org/abs/1506.03134 Attention Is All You Need - https://arxiv.org/abs/1706.03762 The Annotated Transformer - https://nlp.seas.harvard.edu/annotated-transformer/ Neural Turing Machines - https://arxiv.org/abs/1410.5401 A Simple Neural Network Module for Relational Reasoning - https://arxiv.org/abs/1706.01427 Relational Recurrent Neural Networks - https://arxiv.org/abs/1806.01822 Neural Message Passing for Quantum Chemistry - https://arxiv.org/abs/1704.01212 Scaling Laws for Neural Language Models - https://arxiv.org/abs/2001.08361 GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism - https://arxiv.org/abs/1811.06965 Keeping Neural Networks Simple by Minimizing the Description Length of the Weights - https://www.cs.toronto.edu/~hinton/absps/colt93.pdf A Tutorial Introduction to the Minimum Description Length Principle - https://arxiv.org/abs/math/0406077 The First Law of Complexodynamics - https://scottaaronson.blog/?p=762 Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton - https://arxiv.org/abs/1405.6903 Kolmogorov Complexity - https://onlinelibrary.wiley.com/doi/book/10.1002/047174882X Variational Lossy Autoencoder - https://arxiv.org/abs/1611.02731 Machine Super Intelligence - https://www.vetta.org/documents/Machine_Super_Intelligence.p...
Original comment 30papers.com – Ilya's 30 essential ML papers, in a beginner friendly format
espadrine
Neutral mention · rule
Nah. They need to walk before they can run. In 2016, their system could locate objects in a scene[0]. In 2017, it could answer questions about a scene[1]. In 2018, it could infer how a scene looks from any angle using a couple photos[2]. Now, it can perform inference tasks in a 3D scene. Going to the real world will require more work! But they will be helped by converging existing advances[3]. [0]: https://arxiv.org/pdf/1603.08575.pdf [1]: https://arxiv.org/pdf/1706.01427.pdf [2]: https://www.deepmind.com/blog/neural-scene-representation-an... [3]: https://www.deepmind.com/blog/stacking-our-way-to-more-gener...
Original comment DeepMind AI is as fast as humans at solving previously unseen tasks
yonkshi
Neutral mention · rule
Sure. I don't have any holistic survey to prove my point, but an example of recent progress in terms of sample efficiency is this paper[0]. Derivatives of this paper have been used to solve Sudoku[1], Starcraft II[2] and more [3]. This paper enabled more efficient use of data by creating a probabilistic graphical model between logical sets. [0]https://arxiv.org/abs/1706.01427 [1]https://arxiv.org/abs/1711.08028 [2]https://arxiv.org/abs/1806.01830 [3]https://arxiv.org/abs/1806.01822
Original comment The Machine Learning Race Is Really a Data Race
visarga
Neutral mention · rule
Yes, one of the links is from DeepMind: "A simple neural network module for relational reasoning" https://arxiv.org/abs/1706.01427 Another one is from Thomas Kipf: "Graph Convolutional Matrix Completion" https://arxiv.org/abs/1706.02263
Original comment Exploring LSTMs

Keep following the thread.

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