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Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of neural networks, effectively demonstrating they can learn to execute classical algorithms on unseen data coming from the train distribution.
On the shortest spanning subtree of a graph and the traveling salesman problem
Kruskal, J. B · 1956
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Shortest connection networks and some generalizations
Prim, R. C · 1957
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On a routing problem
Bellman, R · 1958
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A note on two problems in connexion with graphs
Dijkstra, E. W. et al · 1959
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The shortest path through a maze
Moore, E. F · 1959
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Algorithm 97: shortest path
Floyd, R. W · 1962
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Quicksort
Hoare, C. A · 1962
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Algorithm 232: heapsort
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Fundamental algorithms
Knuth, D. E · 1973
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The design and analysis of computer algorithms
Aho, A. V., Hopcroft, J. E., and Ullman, J. D · 1974
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Lstm recurrent networks learn simple context-free and context-sensitive languages
Gers, F. A. and Schmidhuber, J · 2001
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Causality
Pearl, J · 2009
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Visual causal feature learning
Chalupka, K., Perona, P., and Eberhardt, F · 2014
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Graves, A., Wayne, G., and Danihelka, I · 2014
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Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., Badia, A. P., Hermann, K. M., Zwols, Y., Ostrovski, G., Cain, A., King, H., Summerfield, C., Blunsom, P., Kavukcuoglu, K., and Hassabis, D · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Recurrent relational networks
Palm, R. B., Paquet, U., and Winther, O · 2017
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Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
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Universal transformers
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., and Kaiser, L · 2019
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Learning a SAT solver from single-bit supervision
Selsam, D., Lamm, M., Bünz, B., Liang, P., de Moura, L., and Dill, D. L · 2019
Cited alongside, same era.
Deep graph infomax
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al · 2020
Adversarial graph augmentation to improve graph contrastive learning
Suresh, S., Li, P., Hao, C., and Neville, J · 2021
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Neural algorithmic reasoning
Veličković, P. and Blundell, C · 2021
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How neural networks extrapolate: From feedforward to graph neural networks
Xu, K., Zhang, M., Li, J., Du, S. S., Kawarabayashi, K.-I., and Jegelka, S · 2021
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From local structures to size generalization in graph neural networks
Yehudai, G., Fetaya, E., Meirom, E., Chechik, G., and Maron, H · 2021
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End-to-end algorithm synthesis with recurrent networks: Logical extrapolation without overthinking
Bansal, A., Schwarzschild, A., Borgnia, E., Emam, Z., Huang, F., Goldblum, M., and Goldstein, T · 2022
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Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Cited alongside, same era.
Learning the travelling salesperson problem requires rethinking generalization
Joshi, C. K., Cappart, Q., Rousseau, L.-M., and Laurent, T · 2020
Cited alongside, same era.
Towards scale-invariant graph-related problem solving by iterative homogeneous gnns
Tang, H., Huang, Z., Gu, J., Lu, B.-L., and Su, H · 2020
Cited alongside, same era.
Neural execution of graph algorithms
Veličković, P., Ying, R., Padovano, M., Hadsell, R., and Blundell, C · 2020
Cited alongside, same era.
What can neural networks reason about?
Xu, K., Li, J., Zhang, M., Du, S. S., ichi Kawarabayashi, K., and Jegelka, S · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
Cited alongside, same era.
Beurer-Kellner, L., Vechev, M., Vanbever, L., and Veličković, P · 2022
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Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks
Buffelli, D., Liò, P., and Vandin, F · 2022
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Learning causally invariant representations for out-of-distribution generalization on graphs
Chen, Y., Zhang, Y., Bian, Y., Yang, H., KAILI, M., Xie, B., Liu, T., Han, B., and Cheng, J · 2022
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Relational attention: Generalizing transformers for graph-structured tasks
Diao, C. and Loynd, R · 2022
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Graph neural networks are dynamic programmers
Dudzik, A. J. and Veličković, P · 2022
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How do graph networks generalize to large and diverse molecular systems?
Gasteiger, J., Shuaibi, M., Sriram, A., Günnemann, S., Ulissi, Z. W., Zitnick, C. L., and Das, A · 2022
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A generalist neural algorithmic learner
Ibarz, B., Kurin, V., Papamakarios, G., Nikiforou, K., Bennani, M., Csordás, R., Dudzik, A. J., Bošnjak, M., Vitvitskyi, A., Rubanova, Y., Deac, A., Bevilacqua, B., Ganin, Y., Blundell, C., and Veličković, P · 2022
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Theory of graph neural networks: Representation and learning
Jegelka, S · 2022
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Towards better out-of-distribution generalization of neural algorithmic reasoning tasks
Mahdavi, S., Swersky, K., Kipf, T., Hashemi, M., Thrampoulidis, C., and Liao, R · 2022
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Large-scale representation learning on graphs via bootstrapping
Thakoor, S., Tallec, C., Azar, M. G., Azabou, M., Dyer, E. L., Munos, R., Veličković, P., and Valko, M · 2022
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Tomasev, N., Bica, I., McWilliams, B., Buesing, L., Pascanu, R., Blundell, C., and Mitrovic, J · 2022
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Self-supervised learning of graph neural networks: A unified review
Xie, Y., Xu, Z., Zhang, J., Wang, Z., and Ji, S · 2022
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Bringing your own view: Graph contrastive learning without prefabricated data augmentations
You, Y., Chen, T., Wang, Z., and Shen, Y · 2022
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OOD link prediction generalization capabilities of message-passing GNNs in larger test graphs
Zhou, Y., Kutyniok, G., and Ribeiro, B · 2022
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