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Neural algorithmic reasoning aims to capture computations with neural networks by training models to imitate the execution of classical algorithms.
A note on two problems in connexion with graphs
Dijkstra, E. W · 1959
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On tables of random numbers
Kolmogorov, A. N · 1963
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Learning to reason
Khardon, R. and Roth, D · 1997
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The importance of complexity in model selection
Myung, I. J · 2000
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Information and complexity in statistical modeling
Rissanen, J · 2006
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Introduction to Algorithms
Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C · 2009
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Graves, A., Wayne, G., and Danihelka, I · 2014
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Zaremba, W. and Sutskever, I · 2014
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Kaiser, Ł. and Sutskever, I · 2015
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Neural programmer-interpreters
Reed, S. and De Freitas, N · 2015
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Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N · 2015
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Dynamic neural Turing machine with soft and hard addressing schemes
Gulcehre, C., Chandar, S., Cho, K., and Bengio, Y · 2016
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Categorical reparameterization with Gumbel-Softmax
Jang, E., Gu, S., and Poole, B · 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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Relational inductive bias for physical construction in humans and machines
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Masked label prediction: Unified message passing model for semi-supervised classification
Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., and Sun, Y · 2020
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What can neural networks reason about?
Xu, K., Li, J., Zhang, M., Du, S. S., Kawarabayashi, K.-i., and Jegelka, S · 2020
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Neural execution engines: Learning to execute subroutines
Yan, Y., Swersky, K., Koutra, D., Ranganathan, P., and Hashemi, M · 2020
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A mathematical framework for transformer circuits
Neural algorithmic reasoning with causal regularisation
Bevilacqua, B., Nikiforou, K., Ibarz, B., Bica, I., Paganini, M., Blundell, C., Mitrovic, J., and Veličković, P · 2023
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Learning transformer programs
Friedman, D., Wettig, A., and Chen, D · 2023
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Beyond Erdös-Rényi: Generalization in algorithmic reasoning on graphs
Georgiev, D., Lio, P., Bachurski, J., Chen, J., and Shi, T · 2023
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Neural priority queues for graph neural networks
Jain, R., Veličković, P., and Liò, P · 2023
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Controlling neural network smoothness for neural algorithmic reasoning
Klindt, D. A · 2023
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Tracr: Compiled transformers as a laboratory for interpretability
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Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., DasSarma, N., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., Amodei, D., Brown, T., Clark, J., Kaplan, J., McCandlish, S., and Olah, C · 2021
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Neural algorithmic reasoning
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Thinking like transformers
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How to transfer algorithmic reasoning knowledge to learn new algorithms?
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Learning to configure computer networks with neural algorithmic reasoning
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Graph neural networks are dynamic programmers
Dudzik, A. J. and Veličković, P · 2022
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Algorithmic concept-based explainable reasoning
Georgiev, D., Barbiero, P., Kazhdan, D., Veličković, P., and Liò, P · 2022
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Lindner, D., Kramár, J., Farquhar, S., Rahtz, M., McGrath, T., and Mikulik, V · 2023
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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 · 2023
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SALSA-CLRS: A sparse and scalable benchmark for algorithmic reasoning
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Dual algorithmic reasoning
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Neural algorithmic reasoning without intermediate supervision
Rodionov, G. and Prokhorenkova, L · 2023
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On the Markov property of neural algorithmic reasoning: Analyses and methods
Bohde, M., Liu, M., Saxton, A., and Ji, S · 2024
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Simulation of graph algorithms with looped transformers
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Recursive algorithmic reasoning
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What algorithms can transformers learn? A study in length generalization
Zhou, H., Bradley, A., Littwin, E., Razin, N., Saremi, O., Susskind, J. M., Bengio, S., and Nakkiran, P · 2024
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