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Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs).
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Learning Systems of Concepts with an Infinite Relational Model
Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., and Ueda, N · 2006
Earlier work this paper cites.
Artificial Intelligence - A Modern Approach, Third International Edition
Russell, S. J. and Norvig, P · 2010
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
Graves, A., Wayne, G., and Danihelka, I · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Kim, Y · 2014
Earlier work this paper cites.
On approximate reasoning capabilities of low-rank vector spaces
Bouchard, G., Singh, S., and Trouillon, T · 2015
Earlier work this paper cites.
Neural-symbolic learning and reasoning: Contributions and challenges
d’Avila Garcez, A. S., Besold, T. R., Raedt, L. D., Földiák, P., Hitzler, P., Icard, T., Kühnberger, K., Lamb, L. C., Miikkulainen, R., and Silver, D. L · 2015
Earlier work this paper cites.
Learning to Transduce with Unbounded Memory
Grefenstette, E., Hermann, K. M., Suleyman, M., and Blunsom, P · 2015
Earlier work this paper cites.
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
Joulin, A. and Mikolov, T · 2015
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E · 2015
Earlier work this paper cites.
Effective approaches to attention-based neural machine translation
Luong, T., Pham, H., and Manning, C. D · 2015
Earlier work this paper cites.
Injecting logical background knowledge into embeddings for relation extraction
Rocktäschel, T., Singh, S., and Riedel, S · 2015
Earlier work this paper cites.
End-To-End Memory Networks
Sukhbaatar, S., Szlam, A., Weston, J., and Fergus, R · 2015
Earlier work this paper cites.
Neural module networks
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D · 2016
Earlier work this paper cites.
Lifted rule injection for relation embeddings
Demeester, T., Rocktäschel, T., and Riedel, S · 2016
Earlier work this paper cites.
Deep Learning
Goodfellow, I. J., Bengio, Y., and Courville, A. C · 2016
Earlier work this paper cites.
Neural GPUs Learn Algorithms
Kaiser, L. and Sutskever, I · 2016
Earlier work this paper cites.
Character-aware neural language models
Kim, Y., Jernite, Y., Sontag, D. A., and Rush, A. M · 2016
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Key-value memory networks for directly reading documents
Miller, A. H., Fisch, A., Dodge, J., Karimi, A., Bordes, A., and Weston, J · 2016
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Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T · 2016
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Reasonet: Learning to stop reading in machine comprehension
Shen, Y., Huang, P., Gao, J., and Chen, W · 2016
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Programming with a Differentiable Forth Interpreter
Bošnjak, M., Rocktäschel, T., Naradowsky, J., and Riedel, S · 2017
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Logic tensor networks for semantic image interpretation
Donadello, I., Serafini, L., and d’Avila Garcez, A. S · 2017
Reinforced mnemonic reader for machine reading comprehension
Hu, M., Peng, Y., Huang, Z., Qiu, X., Wei, F., and Zhou, M · 2018
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Fusionnet: Fusing via fully-aware attention with application to machine comprehension
Huang, H., Zhu, C., Shen, Y., and Chen, W · 2018
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How much reading does reading comprehension require? A critical investigation of popular benchmarks
Kaushik, D. and Lipton, Z. C · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Lake, B. M. and Baroni, M · 2018
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The mythos of model interpretability
Lipton, Z. C · 2018
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
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Neural Network Methods for Natural Language Processing
Goldberg, Y · 2017
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Adversarial examples for evaluating reading comprehension systems
Jia, R. and Liang, P · 2017
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., and Girshick, R. B · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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End-to-end differentiable proving
Rocktäschel, T. and Riedel, S · 2017
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Sodhani, S., Chandar, S., and Bengio, Y · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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A semantic loss function for deep learning with symbolic knowledge
Xu, J., Zhang, Z., Friedman, T., Liang, Y., and den Broeck, G. V · 2018
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Systematic generalization: What is required and can it be learned?
Bahdanau, D., Murty, S., Noukhovitch, M., Nguyen, T. H., de Vries, H., and Courville, A. C · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2019
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Reconciling deep learning with symbolic artificial intelligence: representing objects and relations
Garnelo, M. and Shanahan, M · 2019
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Neural module networks for reasoning over text
Gupta, N., Lin, K., Roth, D., Singh, S., and Gardner, M · 2019
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Self-assembling modular networks for interpretable multi-hop reasoning
Jiang, Y. and Bansal, M · 2019
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Roberta: A robustly optimized BERT pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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DRUM: end-to-end differentiable rule mining on knowledge graphs
Sadeghian, A., Armandpour, M., Ding, P., and Wang, D. Z · 2019
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CLUTRR: A diagnostic benchmark for inductive reasoning from text
Sinha, K., Sodhani, S., Dong, J., Pineau, J., and Hamilton, W. L · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J. G., Salakhutdinov, R., and Le, Q. V · 2019
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Differentiable reasoning on large knowledge bases and natural language
Minervini, P., Bosnjak, M., Rocktäschel, T., Riedel, S., and Grefenstette, E · 2020
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