Fetching the paper…
Reading the bibliography…
A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such as Raven's Progressive Matrices (RPM).
The nature of” intelligence” and the principles of cognition
Charles Spearman · 1923
Earlier work this paper cites.
Progressive matrices: A perceptual test of intelligence, individual form
John C Raven · 1938
Earlier work this paper cites.
Structure-mapping: A theoretical framework for analogy
Dedre Gentner · 1983
Earlier work this paper cites.
Schema induction and analogical transfer
Mary L Gick and Keith J Holyoak · 1983
Earlier work this paper cites.
Selective attention and the organization of visual information
John Duncan · 1984
Earlier work this paper cites.
The topography of ability and learning correlations
Richard E Snow, Patrick C Kyllonen, Brachia Marshalek, et al · 1984
Earlier work this paper cites.
The role of location indexes in spatial perception: A sketch of the finst spatial-index model
Zenon Pylyshyn · 1989
Earlier work this paper cites.
Analogy and relational reasoning
Keith J Holyoak · 2012
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Earlier work this paper cites.
Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Measuring abstract reasoning in neural networks
David Barrett, Felix Hill, Adam Santoro, Ari Morcos, and Timothy Lillicrap · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Improving generalization for abstract reasoning tasks using disentangled feature representations
Xander Steenbrugge, Sam Leroux, Tim Verbelen, and Bart Dhoedt · 2018
Cited alongside, same era.
Abstract diagrammatic reasoning with multiplex graph networks
Duo Wang, Mateja Jamnik, and Pietro Lio · 2020
Later among the works it cites.
Learning representations that support extrapolation
Taylor Webb, Zachary Dulberg, Steven Frankland, Alexander Petrov, Randall O’Reilly, and Jonathan Cohen · 2020
Later among the works it cites.
Yuhuai Wu, Honghua Dong, Roger Grosse, and Jimmy Ba · 2020
Later among the works it cites.
Scale-localized abstract reasoning
Yaniv Benny, Niv Pekar, and Lior Wolf · 2021
Later among the works it cites.
Attention over learned object embeddings enables complex visual reasoning
David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, and Matt Botvinick · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
Cited alongside, same era.
Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
Cited alongside, same era.
Learning to make analogies by contrasting abstract relational structure
Felix Hill, Adam Santoro, David GT Barrett, Ari S Morcos, and Timothy Lillicrap · 2019
Cited alongside, same era.
Are disentangled representations helpful for abstract visual reasoning?
Sjoerd Van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
Cited alongside, same era.
Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loic Matthey, Christopher P Burgess, and Alexander Lerchner · 2019
Cited alongside, same era.
Abstract reasoning with distracting features
Kecheng Zheng, Zheng-Jun Zha, and Wei Wei · 2019
Cited alongside, same era.
Solving raven’s progressive matrices with multi-layer relation networks
Marius Jahrens and Thomas Martinetz · 2020
Cited alongside, same era.
Genesis-v2: Inferring unordered object representations without iterative refinement
Martin Engelcke, Oiwi Parker Jones, and Ingmar Posner · 2021
Later among the works it cites.
Stratified rule-aware network for abstract visual reasoning
Sheng Hu, Yuqing Ma, Xianglong Liu, Yanlu Wei, and Shihao Bai · 2021
Later among the works it cites.
Capturing the objects of vision with neural networks
Benjamin Peters and Nikolaus Kriegeskorte · 2021
Later among the works it cites.
Emergent symbols through binding in external memory
Taylor Webb, Ishan Sinha, and Jonathan D. Cohen · 2021
Later among the works it cites.
Learning algebraic representation for systematic generalization in abstract reasoning
Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, and Yixin Zhu · 2021
Later among the works it cites.
On neural architecture inductive biases for relational tasks
Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake Richards, and Guillaume Lajoie · 2022
Later among the works it cites.
Slotformer: Unsupervised visual dynamics simulation with object-centric models
Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, and Animesh Garg · 2022
Later among the works it cites.
Effective abstract reasoning with dual-contrast network
Tao Zhuo and Mohan Kankanhalli · 2022
Later among the works it cites.