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Attention mechanisms are dominating the explainability of deep models.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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The dynamic representation of scenes
Rensink, R. A · 2000
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Convolutional networks and applications in vision
LeCun, Y., Kavukcuoglu, K., and Farabet, C · 2010
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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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Visualizing deep neural network decisions: Prediction difference analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M · 2013
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Recurrent models of visual attention
Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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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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A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D · 2015
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Effective approaches to attention-based neural machine translation
Luong, T., Pham, H., and Manning, C. D · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K., and Samek, W · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Understanding neural networks through representation erasure
Li, J., Monroe, W., and Jurafsky, D · 2016
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”why should I trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Towards ai-complete question answering: A set of prerequisite toy tasks
Weston, J., Bordes, A., Chopra, S., and Mikolov, T · 2016
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Stacked attention networks for image question answering
Yang, Z., He, X., Gao, J., Deng, L., and Smola, A. J · 2016
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., and Parikh, D · 2017
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Show, ask, attend, and answer: A strong baseline for visual question answering
Kazemi, V. and Elqursh, A · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F. B., and Wattenberg, M · 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, L., and Polosukhin, I · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I. J., Hardt, M., and Kim, B · 2018
Attention is not not explanation
Wiegreffe, S. and Pinter, Y · 2019
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Quantifying attention flow in transformers
Abnar, S. and Zuidema, W. H · 2020
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A diagnostic study of explainability techniques for text classification
Atanasova, P., Simonsen, J. G., Lioma, C., and Augenstein, I · 2020
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On identifiability in transformers
Brunner, G., Liu, Y., Pascual, D., Richter, O., Ciaramita, M., and Wattenhofer, R · 2020
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ERASER: A benchmark to evaluate rationalized NLP models
DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., and Wallace, B. C · 2020
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Jacovi, A. and Goldberg, Y · 2020
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Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Öztireli, C., and Gross, M · 2018
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Bottom-up and top-down attention for image captioning and visual question answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., and Zhang, L · 2018
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L-shapley and c-shapley: Efficient model interpretation for structured data
Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I · 2019
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What does BERT look at? an analysis of bert’s attention
Clark, K., Khandelwal, U., Levy, O., and Manning, C. D · 2019
Cited alongside, same era.
Understanding deep networks via extremal perturbations and smooth masks
Fong, R., Patrick, M., and Vedaldi, A · 2019
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Hooker, S., Erhan, D., Kindermans, P., and Kim, B · 2019
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Attention is not only a weight: Analyzing transformers with vector norms
Kobayashi, G., Kuribayashi, T., Yokoi, S., and Inui, K · 2020
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What does bert with vision look at?
Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., and Chang, K.-W · 2020
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Towards transparent and explainable attention models
Mohankumar, A. K., Nema, P., Narasimhan, S., Khapra, M. M., Srinivasan, B. V., and Ravindran, B · 2020
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2020
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When explanations lie: Why many modified BP attributions fail
Sixt, L., Granz, M., and Landgraf, T · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swayamdipta, S., Schwartz, R., Lourie, N., Wang, Y., Hajishirzi, H., Smith, N. A., and Choi, Y · 2020
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Sanity checks for saliency metrics
Tomsett, R., Harborne, D., Chakraborty, S., Gurram, P., and Preece, A. D · 2020
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Why attentions may not be interpretable?
Bai, B., Liang, J., Zhang, G., Li, H., Bai, K., and Wang, F · 2021
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Improving the faithfulness of attention-based explanations with task-specific information for text classification
Chrysostomou, G. and Aletras, N · 2021
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Attention flows are shapley value explanations
Ethayarajh, K. and Jurafsky, D · 2021
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Self-attention attribution: Interpreting information interactions inside transformer
Hao, Y., Dong, L., Wei, F., and Xu, K · 2021
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The out-of-distribution problem in explainability and search methods for feature importance explanations
Hase, P., Xie, H., and Bansal, M · 2021
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Measuring and improving faithfulness of attention in neural machine translation
Moradi, P., Kambhatla, N., and Sarkar, A · 2021
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Rethinking the role of gradient-based attribution methods for model interpretability
Srinivas, S. and Fleuret, F · 2021
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