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Graph Neural Networks (GNNs) have shown considerable success in neural algorithmic reasoning.
What can neural networks reason about?, 2019
Xu, K., Li, J., Zhang, M., Du, S. S., Kawarabayashi, K.-i., and Jegelka, S · 1905
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Neural execution of graph algorithms, 2019
Veličković, P., Ying, R., Padovano, M., Hadsell, R., and Blundell, C · 1910
Earlier work this paper cites.
Temporal graph networks for deep learning on dynamic graphs, 2020
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., and Bronstein, M · 2006
Earlier work this paper cites.
Veličković, P., Buesing, L., Overlan, M. C., Pascanu, R., Vinyals, O., and Blundell, C · 2006
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate, 2014
Bahdanau, D., Cho, K., and Bengio, Y · 2014
Earlier work this paper cites.
Graves, A., Wayne, G., and Danihelka, I · 2014
Earlier work this paper cites.
Learning to transduce with unbounded memory, 2015
Grefenstette, E., Hermann, K. M., Suleyman, M., and Blunsom, P · 2015
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Inferring algorithmic patterns with stack-augmented recurrent nets, 2015
Joulin, A. and Mikolov, T · 2015
Earlier work this paper cites.
Gated graph sequence neural networks, 2015
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2015
Cited alongside, same era.
Attention is all you need, 2017
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Relational inductive bias for physical construction in humans and machines, 2018
Hamrick, J. B., Allen, K. R., Bapst, V., Zhu, T., McKee, K. R., Tenenbaum, J. B., and Battaglia, P. W · 2018
Cited alongside, same era.
Graph attention networks, 2018
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Cited alongside, same era.
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R., and Smola, A · 2018
Cited alongside, same era.
Persistent message passing, 2021
Strathmann, H., Barekatain, M., Blundell, C., and Veličković, P · 2021
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Neural algorithmic reasoning
Veličković, P. and Blundell, C · 2021
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How attentive are graph attention networks?, 2022
Brody, S., Alon, U., and Yahav, E · 2022
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Introduction to algorithms
Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C · 2022
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Long range graph benchmark, 2022
Dwivedi, V. P., Rampášek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A. T., and Beaini, D · 2022
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A generalist neural algorithmic learner, 2022
Ibarz, B., Kurin, V., Papamakarios, G., Nikiforou, K., Bennani, M., Csordás, R., Dudzik, A., Bošnjak, M., Vitvitskyi, A., Rubanova, Y., Deac, A., Bevilacqua, B., Ganin, Y., Blundell, C., and Veličković, P · 2022
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Can graph neural networks count substructures?
Chen, Z., Chen, L., Villar, S., and Bruna, J · 2020
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2021
Cited alongside, same era.
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The clrs algorithmic reasoning benchmark, 2022
Veličković, P., Badia, A. P., Budden, D., Pascanu, R., Banino, A., Dashevskiy, M., Hadsell, R., and Blundell, C · 2022
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