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Transformers have become an important workhorse of machine learning, with numerous applications.
A Value for n-Person Games , pp. 307–318
Shapley, L. S · 1953
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An efficient explanation of individual classifications using game theory
Strumbelj, E. and Kononenko, I · 2010
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Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C · 2013
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Extraction of salient sentences from labelled documents
Denil, M., Demiraj, A., and de Freitas, N · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., and Kalai, A · 2016
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Computational modeling of β \beta -secretase 1 (bace-1) inhibitors using ligand based approaches
Subramanian, G., Ramsundar, B., Pande, V., and Denny, R. A · 2016
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Visualizing and understanding neural machine translation
Ding, Y., Liu, Y., Luan, H., and Sun, M · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., and Bronstein, M. M · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 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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Pathologies of neural models make interpretations difficult
Feng, S., Wallace, E., Grissom II, A., Iyyer, M., Rodriguez, P., and Boyd-Graber, J · 2018
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Examining gender and race bias in two hundred sentiment analysis systems
Kiritchenko, S. and Mohammad, S · 2018
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Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., and Müller, K · 2018
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Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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Gradient-based attribution methods
Ancona, M., Ceolini, E., Öztireli, C., and Gross, M. H · 2019
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Explaining and Interpreting LSTMs
Arras, L., Arjona-Medina, J., Widrich, M., Montavon, G., Gillhofer, M., Müller, K.-R., Hochreiter, S., and Samek, W · 2019
Cited alongside, same era.
Bias in bios: A case study of semantic representation bias in a high-stakes setting
De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., and Kalai, A. T · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Gonen, H. and Goldberg, Y · 2019
Cited alongside, same era.
A survey of methods for explaining black box models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D · 2019
Cited alongside, same era.
Attention is not Explanation
A diagnostic study of explainability techniques for text classification
Atanasova, P., Simonsen, J. G., Lioma, C., and Augenstein, I · 2020
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Hierarchical pre-training for sequence labelling in spoken dialog
Chapuis, E., Colombo, P., Manica, M., Labeau, M., and Clavel, C · 2020
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A survey of the state of explainable AI for natural language processing
Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., and Sen, P · 2020
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Graphlime: Local interpretable model explanations for graph neural networks
Huang, Q., Yamada, M., Tian, Y., Singh, D., Yin, D., and Chang, Y · 2020
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Parameterized explainer for graph neural network
Luo, D., Cheng, W., Xu, D., Yu, W., Zong, B., Chen, H., and Zhang, X · 2020
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Jain, S. and Wallace, B. C · 2019
Cited alongside, same era.
Gradient-based vs. propagation-based explanations: An axiomatic comparison
Montavon, G · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks
Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., and Hoffmann, H · 2019
Cited alongside, same era.
Perturbation sensitivity analysis to detect unintended model biases
Prabhakaran, V., Hutchinson, B., and Mitchell, M · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning , volume 11700 of Lecture Notes in Computer Science
Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., and Müller, K.-R. (eds.) · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2019
Cited alongside, same era.
Maziarka, Ł., Danel, T., Mucha, S., Rataj, K., Tabor, J., and Jastrzkebski, S · 2020
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Beyond accuracy: Behavioral testing of nlp models with checklist
Ribeiro, M. T., Wu, T. S., Guestrin, C., and Singh, S · 2020
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Graph-aware transformer: Is attention all graphs need?
Yoo, S.-Y., Kim, Y.-S., Lee, K., Jeong, K., Choi, J., Lee, H., and Choi, Y. S · 2020
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Airm: a new ai recruiting model for the saudi arabia labor market
Aleisa, M. A., Beloff, N., and White, M · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Fast axiomatic attribution for neural networks
Hesse, R., Schaub-Meyer, S., and Roth, S · 2021
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Relative importance in sentence processing
Hollenstein, N. and Beinborn, L · 2021
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Efficient large-scale language model training on GPU clusters using megatron-lm
Narayanan, D., Shoeybi, M., Casper, J., LeGresley, P., Patwary, M., Korthikanti, V., Vainbrand, D., Kashinkunti, P., Bernauer, J., Catanzaro, B., Phanishayee, A., and Zaharia, M · 2021
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Probing toxic content in large pre-trained language models
Ousidhoum, N., Zhao, X., Fang, T., Song, Y., and Yeung, D.-Y · 2021
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Explaining deep neural networks and beyond: A review of methods and applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., and Müller, K.-R · 2021
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Higher-order explanations of graph neural networks via relevant walks
Schnake, T., Eberle, O., Lederer, J., Nakajima, S., Schütt, K. T., Müller, K.-R., and Montavon, G · 2021
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On explaining your explanations of bert: An empirical study with sequence classification
Wu, Z. and Ong, D. C · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Gophormer: Ego-graph transformer for node classification
Zhao, J., Li, C., Wen, Q., Wang, Y., Liu, Y., Sun, H., Xie, X., and Ye, Y · 2021
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