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Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms.
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 65: find
Hoare, C. A · 1961
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Algorithm 97: shortest path
Floyd, R. W · 1962
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Quicksort
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Algorithm 232: heapsort
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Algorithms for minimum coloring, maximum clique, minimum covering by cliques, and maximum independent set of a chordal graph
Gavril, F · 1972
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Graham, R. L · 1972
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On the identification of the convex hull of a finite set of points in the plane
Jarvis, R. A · 1973
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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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Fast pattern matching in strings
Knuth, D. E., Morris, Jr, J. H., and Pratt, V. R · 1977
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Programming pearls: algorithm design techniques
Bentley, J · 1984
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The traveling salesman problem: a guided tour of combinatorial optimization
Lawler, E. L · 1985
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On the evolution of random graphs
Erdös, P. and Rényi, A · 2011
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Empirical evaluation and combination of advanced language modeling techniques
Mikolov, T., Deoras, A., Kombrink, S., Burget, L., and Cernocký, J · 2011
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
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Graves, A., Wayne, G., and Danihelka, I · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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Zaremba, W. and Sutskever, I · 2014
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Flows in networks
Ford Jr, L. R. and Fulkerson, D. R · 2015
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Kaiser, Ł. and Sutskever, I · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Sukhbaatar, S., Szlam, A., Weston, J., and Fergus, R · 2015
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Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N · 2015
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Pointer sentinel mixture models
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
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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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Graph neural induction of value iteration
Deac, A., Bacon, P.-L., and Tang, J · 2020
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., and Bresson, X · 2020
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Georgiev, D. and Lió, P · 2020
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Haiku: Sonnet for JAX, 2020
Hennigan, T., Cai, T., Norman, T., and Babuschkin, I · 2020
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Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Learning combinatorial optimization algorithms over graphs
Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Modular meta-learning
Alet, F., Lozano-Perez, T., and Kaelbling, L. P · 2018
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Adapting auxiliary losses using gradient similarity, 2018
Du, Y., Czarnecki, W. M., Jayakumar, S. M., Pascanu, R., and Lakshminarayanan, B · 2018
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Learning tsp requires rethinking generalization
Joshi, C. K., Cappart, Q., Rousseau, L.-M., Laurent, T., and Bresson, X · 2020
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Strong generalization and efficiency in neural programs
Li, Y., Gimeno, F., Kohli, P., and Vinyals, O · 2020
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Liu, C., Zhu, L., and Belkin, M · 2020
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Normalized attention without probability cage
Richter, O. and Wattenhofer, R · 2020
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Towards scale-invariant graph-related problem solving by iterative homogeneous gnns
Tang, H., Huang, Z., Gu, J., Lu, B., and Su, H · 2020
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Veličković, P., Buesing, L., Overlan, M. C., Pascanu, R., Vinyals, O., and Blundell, C · 2020
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How neural networks extrapolate: From feedforward to graph neural networks
Xu, K., Li, J., Zhang, M., Du, S. S., ichi Kawarabayashi, K., and Jegelka, S · 2020
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Neural execution engines: Learning to execute subroutines
Yan, Y., Swersky, K., Koutra, D., Ranganathan, P., and Heshemi, M · 2020
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It’s not what machines can learn, it’s what we cannot teach
Yehuda, G., Gabel, M., and Schuster, A · 2020
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Banino, A., Balaguer, J., and Blundell, C · 2021
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Size-invariant graph representations for graph classification extrapolations
Bevilacqua, B., Zhou, Y., and Ribeiro, B · 2021
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How attentive are graph attention networks?
Brody, S., Alon, U., and Yahav, E · 2021
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Combinatorial optimization and reasoning with graph neural networks
Cappart, Q., Chételat, D., Khalil, E., Lodi, A., Morris, C., and Veličković, P · 2021
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Neural algorithmic reasoners are implicit planners
Deac, A.-I., Veličković, P., Milinkovic, O., Bacon, P.-L., Tang, J., and Nikolic, M · 2021
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Simple gnn regularisation for 3d molecular property prediction and beyond
Godwin, J., Schaarschmidt, M., Gaunt, A. L., Sanchez-Gonzalez, A., Rubanova, Y., Veličković, P., Kirkpatrick, J., and Battaglia, P · 2021
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Strathmann, H., Barekatain, M., Blundell, C., and Veličković, P · 2021
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Veličković, P. and Blundell, C · 2021
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Reasoning-modulated representations
Veličković, P., Bošnjak, M., Kipf, T., Lerchner, A., Hadsell, R., Pascanu, R., and Blundell, C · 2021
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How to transfer algorithmic reasoning knowledge to learn new algorithms?
Xhonneux, L.-P., Deac, A.-I., Veličković, P., and Tang, J · 2021
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Graph neural networks are dynamic programmers
Dudzik, A. and Veličković, P · 2022
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