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In artificial intelligence (AI), knowledge is the information required by an intelligent system to accomplish tasks.
Building Expert Systems
Hayes-Roth, F., Waterman, D., and Lenat, D. (1983) · 1983
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Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project
Lenat, D. B. and Guha, R. V. (1990) · 1990
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Building large knowledge bases by mass collaboration
Richardson, M. and Domingos, P. (2003) · 2003
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Reducing the dimensionality of data with neural networks
Hinton, G. and Salakhutdinov, R. (2006) · 2006
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Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. (2008) · 2008
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Language models are open knowledge graphs
Wang, C., Liu, X., and Song, D. (2020) · 2010
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., and Acero, A. (2011) · 2011
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Computer Architecture: A Quantitative Approach
Hennessy, J. L. and Patterson, D. A. (2011) · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. (2012) · 2012
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014) · 2014
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Convolutional neural networks for sentence classification
Kim, Y. (2014) · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2015) · 2015
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Knowlife: A versatile approach for constructing a large knowledge grapha for biomedical sciences
Ernst, P., Siu, A., and Weikum, G. (2015) · 2015
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End-to-end memory networks
Sukhbaatar, S., Weston, J., Fergus, R., et al. (2015) · 2015
Cited alongside, same era.
Memeory networks
Weston, J., Chopra, S., and Bordes, A. (2015) · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y. (2015) · 2015
Cited alongside, same era.
Ask me anything: Dynamic memory networks for natural language processing
Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., and Socher, R. (2016) · 2016
Cited alongside, same era.
Assessing the ability of lstms to learn syntax-sensitive dependencies
Linzen, T., Dupoux, E., and Goldberg, Y. (2016) · 2016
Cited alongside, same era.
Recurrent neural network for text classification with multi-task learning
Liu, P., Qiu, X., and Huang, X. (2016) · 2016
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
Later among the works it cites.
Commonsense knowledge mining from pretrained models
Feldman, J., Davison, J., and Rush, A. (2019) · 2019
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A structural probe for finding syntax in word representations
Hewitt, J. and Manning, C. (2019) · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2019) · 2019
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Language models as knowledge bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., and Miller, A. (2019) · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019) · 2019
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Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A. (2016) · 2016
Cited alongside, same era.
What do neural machine translation models learn about morphology?
Belinkov, Y., Durrani, N., Dalvi, F., Sajjad, H., and Glass, J. (2017) · 2017
Cited alongside, same era.
The more you know: Using knowledge graphs for image classification
Marino, K., Salakhutdinov, R., and Gupta, A. (2017) · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
Deep rnns encode soft hierarchical syntax
Blevins, T., Levy, O., and Zettlemoyer, L. (2018) · 2018
Cited alongside, same era.
Style transfer in text: Exploration and evaluation
Fu, Z., Tan, X., Peng, N., Zhao, D., and Yan, R. (2018) · 2018
Cited alongside, same era.
Inducing relational knowledge from BERT
Bouraoui, Z., Camacho-Collados, J., and Schockaert, S. (2020) · 2020
Closest in time.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020) · 2020
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TinyBERT: Distilling bert for natural language understanding
Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., and Liu, Q. (2020) · 2020
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ALBERT: A lite bert for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2020) · 2020
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On memory in human and artificial language processing systems
Nematzadeh, A., Ruder, S., and Yogatama, D. (2020) · 2020
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A primer in bertology: What we know about how bert works
Rogers, A., Kovaleva, O., and Rumshisky, A. (2020) · 2020
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Network-to-network translation with conditional invertible neural networks
Rombach, R. and Esser, P. (2020) · 2020
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