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We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsity-inducing constraints.
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
Watters, N., Matthey, L., Burgess, C. P., and Lerchner, A · 1901
Earlier work this paper cites.
An Explicitly Relational Neural Network Architecture
Shanahan, M., Nikiforou, K., Creswell, A., et al · 1905
Earlier work this paper cites.
Contrastive Learning of Structured World Models
Kipf, T., van der Pol, E., and Welling, M · 1911
Earlier work this paper cites.
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J · 1984
Earlier work this paper cites.
Language Models are Few-Shot Learners, 2020
Brown, T. B., Mann, B., Ryder, N., et al · 2005
Earlier work this paper cites.
Object-Centric Learning with Slot Attention
Locatello, F., Weissenborn, D., Unterthiner, T., et al · 2006
Earlier work this paper cites.
Scikit-learn: Machine learning in Python, 2011
Pedregosa, F., Varoquaux, G., Gramfort, A., et al · 2011
Earlier work this paper cites.
Neurosymbolic AI: The 3rd Wave
d’Avila Garcez, A. and Lamb, L. C · 2012
Earlier work this paper cites.
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Greff, K., van Steenkiste, S., and Schmidhuber, J · 2012
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., et al · 2015
Cited alongside, same era.
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
Martins, A. F. T. and Astudillo, R. F · 2016
Cited alongside, same era.
Categorical Reparameterization with Gumbel-Softmax
Jang, E., Gu, S., and Poole, B · 2017
Cited alongside, same era.
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Johnson, J., Hariharan, B., Van Der Maaten, L., et al · 2017
The Bitter Lesson, March 2019
Sutton, R · 2019
Later among the works it cites.
Haiku: Sonnet for JAX, 2020
Hennigan, T., Cai, T., Norman, T., and Babuschkin, I · 2020
Later among the works it cites.
Optax: composable gradient transformation and optimisation, in jax!, 2020
Hessel, M., Budden, D., Viola, F., Rosca, M., Sezener, E., and Hennigan, T · 2020
Later among the works it cites.
Sort-of-clevr reimplementation
Tripathi, R. M · 2020
Later among the works it cites.
Neural Production Systems
Goyal, A., Didolkar, A., Ke, N., et al · 2021
Later among the works it cites.
The Defeat of the Winograd Schema Challenge, 2022
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Cited alongside, same era.
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Bradbury, J., Frostig, R., Hawkins, P., et al · 2018
Cited alongside, same era.
A Framework for the Quantitative Evaluation of Disentangled Representations
Eastwood, C. and Williams, C. K. I · 2018
Cited alongside, same era.
Artificial Intelligence and the Common Sense of Animals
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