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A set is an unordered collection of unique elements--and yet many machine learning models that generate sets impose an implicit or explicit ordering.
Probabilistic symmetry and invariant neural networks
Bloem-Reddy, B. and Teh, Y. W · 1901
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E. T., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 1912
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On layer normalization in the transformer architecture
Xiong, R., Yang, Y., He, D., Zheng, K., xin Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L.-W., and Liu, T.-Y · 2002
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Towards differentiable resampling
Zhu, M. X., Murphy, K., and Jonschkowski, R · 2004
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End-to-end object detection with transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A. M., and Zagoruyko, S · 2005
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Object-centric learning with slot attention
Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., and Kipf, T · 2006
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Max-product for maximum weight matching: Convergence, correctness, and lp duality
Bayati, M., Shah, D., and Sharma, M · 2008
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Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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The dependent dirichlet process mixture of objects for detection-free tracking and object modeling
Neiswanger, W., Wood, F., and Xing, E · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Ba, J., Kiros, J. R., and Hinton, G. E · 2016
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Attend, infer, repeat: Fast scene understanding with generative models
Eslami, S. M. A., Heess, N. M. O., Weber, T., Tassa, Y., Szepesvari, D., Kavukcuoglu, K., and Hinton, G. E · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M · 2016
Cited alongside, same era.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J. E., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., and Girshick, R. B · 2017
Learning representations and generative models for 3d point clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L. J · 2018
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Neural network models of exchangeable sequences
Bloem-Reddy, B. and Teh, Y. W · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., and Wanderman-Milne, S · 2018
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Rezatofighi, S. H., Kaskman, R., Motlagh, F. T., Shi, Q., Cremers, D., Leal-Taixé, L., and Reid, I. D · 2018
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
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A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P. W., and Lillicrap, T. P · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Póczos, B., Salakhutdinov, R., and Smola, A. J · 2017
Cited alongside, same era.
Deep set prediction networks
Zhang, Y., Hare, J. S., and Prügel-Bennett, A · 2019
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Haiku: Sonnet for JAX, 2020
Hennigan, T., Cai, T., Norman, T., and Babuschkin, I · 2020
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Machine learning for molecular simulation
Noé, F., Tkatchenko, A., Müller, K.-R., and Clementi, C · 2020
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Fspool: Learning set representations with featurewise sort pooling
Zhang, Y., Hare, J. S., and Prügel-Bennett, A · 2020
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