{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:48:13Z","timestamp":1771516093371,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS-1838177,1730574"],"award-info":[{"award-number":["IIS-1838177,1730574"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Vannevar Bush Faculty Fellowship"},{"name":"MURI","award":["N00014-20-1-2787"],"award-info":[{"award-number":["N00014-20-1-2787"]}]},{"name":"NSF","award":["CCF-1911094"],"award-info":[{"award-number":["CCF-1911094"]}]},{"name":"ONR","award":["N00014-18-12571,N00014-20-1-2534,N00014-18-1-2047"],"award-info":[{"award-number":["N00014-18-12571,N00014-20-1-2534,N00014-18-1-2047"]}]},{"name":"AFOSR","award":["FA9550-22-1-0060"],"award-info":[{"award-number":["FA9550-22-1-0060"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,21]]},"DOI":"10.1145\/3531146.3533224","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T14:27:10Z","timestamp":1655735230000},"page":"1683-1697","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["NeuroView-RNN: It\u2019s About Time"],"prefix":"10.1145","author":[{"given":"Cj","family":"Barberan","sequence":"first","affiliation":[{"name":"Rice University, USA"}]},{"given":"Sina","family":"Alemmohammad","sequence":"additional","affiliation":[{"name":"Rice University, USA"}]},{"given":"Naiming","family":"Liu","sequence":"additional","affiliation":[{"name":"Rice University, USA"}]},{"given":"Randall","family":"Balestriero","sequence":"additional","affiliation":[{"name":"Rice University, USA"}]},{"given":"Richard","family":"Baraniuk","sequence":"additional","affiliation":[{"name":"Rice University, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"S. Alemohammad R. Balestriero Z. Wang and R.\u00a0G. Baraniuk. 2020. Scalable Neural Tangent Kernel of Recurrent Architectures. arXiv preprint arXiv:2012.04859(2020)."},{"key":"e_1_3_2_1_2_1","unstructured":"S. Alemohammad Z. Wang R. Balestriero and R.G. Baraniuk. 2020. The recurrent neural tangent kernel. arXiv preprint arXiv:2006.10246(2020)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"L. Arras J. Arjona-Medina M. Widrich G. Montavon M. Gillhofer K.R. M\u00fcller S. Hochreiter and W. Samek. 2019. Explaining and interpreting LSTMs. In Explainable ai: Interpreting explaining and visualizing deep learning. Springer 211\u2013238.","DOI":"10.1007\/978-3-030-28954-6_11"},{"key":"e_1_3_2_1_4_1","unstructured":"C. Barberan R. Balestriero and R.\u00a0G Baraniuk. 2021. NeuroView: Explainable Deep Network Decision Making. arXiv preprint arXiv:2110.07778(2021)."},{"key":"e_1_3_2_1_5_1","volume-title":"van\u00a0der Schaar","author":"Bica I.","year":"2020","unstructured":"I. Bica, A.\u00a0M. Alaa, J. Jordon, and M. van\u00a0der Schaar. 2020. Estimating counterfactual treatment outcomes over time through adversarially balanced representations. arXiv preprint arXiv:2002.04083(2020)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 9824\u20139833","author":"Chen G.","unstructured":"G. Chen, J. Li, J. Lu, and J. Zhou. 2021. Human Trajectory Prediction via Counterfactual Analysis. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 9824\u20139833."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"K. Cho B. Van\u00a0Merri\u00ebnboer C. Gulcehre D. Bahdanau F. Bougares H. Schwenk and Y. Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014).","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_1_9_1","unstructured":"J. Chung C. Gulcehre K. Cho and Y. Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"H.\u00a0A. Dau E. Keogh K. Kamgar C.-C.\u00a0M. Yeh Y. Zhu S. Gharghabi C.\u00a0A. Ratanamahatana Y. Chen B. Hu N. Begum A. Bagnall A. Mueen G. Batista and M.\u00a0L. Hexagon. 2019. The UCR Time Series Classification Archive. https:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data_2018\/.","DOI":"10.1109\/JAS.2019.1911747"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4306\u20134314","author":"Dong Y.","unstructured":"Y. Dong, H. Su, J. Zhu, and B. Zhang. 2017. Improving interpretability of deep neural networks with semantic information. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4306\u20134314."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 1136\u20131145","author":"Foerster J.N.","unstructured":"J.N. Foerster, J. Gilmer, J. Sohl-Dickstein, J. Chorowski, and D. Sussillo. 2017. Input switched affine networks: An rnn architecture designed for interpretability. In International Conference on Machine Learning (ICML). PMLR, 1136\u20131145."},{"key":"e_1_3_2_1_14_1","volume-title":"2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 177\u2013186","author":"Gammulle H.","unstructured":"H. Gammulle, S. Denman, S. Sridharan, and C. Fookes. 2017. Two stream lstm: A deep fusion framework for human action recognition. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 177\u2013186."},{"key":"e_1_3_2_1_15_1","volume-title":"2018 IEEE 5th International Conference on data science and advanced analytics (DSAA). IEEE, 80\u201389","author":"Gilpin H.","unstructured":"L.\u00a0H. Gilpin, D. Bau, B.\u00a0Z. Yuan, A. Bajwa, M. Specter, and L. Kagal. 2018. Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA). IEEE, 80\u201389."},{"key":"e_1_3_2_1_16_1","unstructured":"G.\u00a0B. Goh N.\u00a0O. Hodas C. Siegel and A. Vishnu. 2017. Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties. arXiv preprint arXiv:1712.02034(2017)."},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 2454\u20132463","author":"Guan C.","unstructured":"C. Guan, X. Wang, Q. Zhang, R. Chen, D. He, and X. Xie. 2019. Towards a deep and unified understanding of deep neural models in nlp. In International Conference on Machine Learning (ICML). PMLR, 2454\u20132463."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 2494\u20132504","author":"Guo T.","unstructured":"T. Guo, T. Lin, and N. Antulov-Fantulin. 2019. Exploring interpretable LSTM neural networks over multi-variable data. In International Conference on Machine Learning (ICML). PMLR, 2494\u20132504."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 3193\u20133207","author":"Jiang C.","unstructured":"C. Jiang, Y. Zhao, S. Chu, L. Shen, and K. Tu. 2020. Cold-start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 3193\u20133207."},{"key":"e_1_3_2_1_21_1","volume-title":"ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 316\u2013320","author":"Kao C.-C.","unstructured":"C.-C. Kao, M. Sun, W. Wang, and C. Wang. 2020. A comparison of pooling methods on LSTM models for rare acoustic event classification. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 316\u2013320."},{"key":"e_1_3_2_1_22_1","unstructured":"Q.\u00a0V. Le N. Jaitly and G.\u00a0E. Hinton. 2015. A simple way to initialize recurrent networks of rectified linear units. arXiv preprint arXiv:1504.00941(2015)."},{"key":"e_1_3_2_1_23_1","volume-title":"Recurrent neural networks with interpretable cells predict and classify worm behaviour. BioRxiv","author":"Li A.","year":"2017","unstructured":"A. Li, K.and\u00a0Javer, E.\u00a0E. Keaveny, and A.E.X. Brown. 2017. Recurrent neural networks with interpretable cells predict and classify worm behaviour. BioRxiv (2017), 222208."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV). 513\u2013528","author":"Li Y.","unstructured":"Y. Li, Y. Li, and N. Vasconcelos. 2018. Resound: Towards action recognition without representation bias. In Proceedings of the European Conference on Computer Vision (ECCV). 513\u2013528."},{"key":"e_1_3_2_1_25_1","volume-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE","author":"Liu J.","unstructured":"J. Liu, J. Luo, and M. Shah. 2009. Recognizing realistic actions from videos \u201cin the wild\u201d. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1996\u20132003."},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 1903\u20131911","author":"Ma F.","unstructured":"F. Ma, R. Chitta, J. Zhou, Q. You, T. Sun, and J. Gao. 2017. Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 1903\u20131911."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6260-6"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics","author":"Maas L.","unstructured":"A.\u00a0L. Maas, R.\u00a0E. Daly, P.\u00a0T. Pham, D. Huang, A.\u00a0Y. Ng, and C. Potts. 2011. Learning Word Vectors for Sentiment Analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Portland, Oregon, USA, 142\u2013150. http:\/\/www.aclweb.org\/anthology\/P11-1015"},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops. 0\u20130.","author":"Meng L.","unstructured":"L. Meng, B. Zhao, B. Chang, G. Huang, W. Sun, F. Tung, and L. Sigal. 2019. Interpretable spatio-temporal attention for video action recognition. In Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops. 0\u20130."},{"key":"e_1_3_2_1_30_1","unstructured":"T. Mikolov K. Chen G. Corrado and J. Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013)."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (ACM SIGKDD). 903\u2013913","author":"Ming Y.","unstructured":"Y. Ming, P. Xu, H. Qu, and L. Ren. 2019. Interpretable and steerable sequence learning via prototypes. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (ACM SIGKDD). 903\u2013913."},{"key":"e_1_3_2_1_32_1","unstructured":"J. Nam H. Cha S. Ahn J. Lee and J. Shin. 2020. Learning from failure: Training debiased classifier from biased classifier. arXiv preprint arXiv:2007.02561(2020)."},{"key":"e_1_3_2_1_33_1","unstructured":"A. Nematzadeh S.\u00a0C Meylan and T.\u00a0L Griffiths. 2017. Evaluating Vector-Space Models of Word Representation or The Unreasonable Effectiveness of Counting Words Near Other Words.. In CogSci."},{"key":"e_1_3_2_1_34_1","unstructured":"R. Pascanu C. Gulcehre K. Cho and Y. Bengio. 2013. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026(2013)."},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 1310\u20131318","author":"Pascanu R.","unstructured":"R. Pascanu, T. Mikolov, and Y. Bengio. 2013. On the difficulty of training recurrent neural networks. In International Conference on Machine Learning (ICML). PMLR, 1310\u20131318."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssc.12511"},{"key":"e_1_3_2_1_38_1","first-page":"2673","article-title":"Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing (IEEE Trans","volume":"45","author":"Schuster M.","year":"1997","unstructured":"M. Schuster and K.\u00a0K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing (IEEE Trans. Signal Process) 45, 11(1997), 2673\u20132681.","journal-title":"Signal Process)"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"P. Schwab G.\u00a0C. Scebba J. Zhang M. Delai and W. Karlen. 2017. Beat by beat: Classifying cardiac arrhythmias with recurrent neural networks. In 2017 Computing in Cardiology (CinC). IEEE 1\u20134.","DOI":"10.22489\/CinC.2017.363-223"},{"key":"e_1_3_2_1_40_1","unstructured":"L. Shen and J. Zhang. 2016. Empirical evaluation of RNN architectures on sentence classification task. arXiv preprint arXiv:1609.09171(2016)."},{"key":"e_1_3_2_1_41_1","unstructured":"T. Shi and Z. Liu. 2014. Linking GloVe with word2vec. arXiv preprint arXiv:1411.5595(2014)."},{"key":"e_1_3_2_1_42_1","volume-title":"32nd Conference on Neural Information Processing Systems (NuerIPS 2018), IRASL workshop.","author":"Shin J.","unstructured":"J. Shin, A. Madotto, and P. Fung. 2018. Interpreting word embeddings with eigenvector analysis. In 32nd Conference on Neural Information Processing Systems (NuerIPS 2018), IRASL workshop."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2851077"},{"key":"e_1_3_2_1_44_1","unstructured":"S.S. Talathi and A. Vartak. 2015. Improving performance of recurrent neural network with relu nonlinearity. arXiv preprint arXiv:1511.03771(2015)."},{"key":"e_1_3_2_1_45_1","unstructured":"S. Tonekaboni S. Joshi D. Duvenaud and A. Goldenberg. 2019. Explaining time series by counterfactuals. (2019)."},{"key":"e_1_3_2_1_46_1","volume-title":"Proceedings of the 24th ACM International Conference on Multimedia (Proc ACM Int Conf Multimed). 988\u2013997","author":"Wang C.","unstructured":"C. Wang, H. Yang, C. Bartz, and C. Meinel. 2016. Image captioning with deep bidirectional LSTMs. In Proceedings of the 24th ACM International Conference on Multimedia (Proc ACM Int Conf Multimed). 988\u2013997."},{"key":"e_1_3_2_1_47_1","volume-title":"ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 31\u201335","author":"Wang Y.","unstructured":"Y. Wang, J. Li, and F. Metze. 2019. A comparison of five multiple instance learning pooling functions for sound event detection with weak labeling. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 31\u201335."}],"event":{"name":"FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency","location":"Seoul Republic of Korea","acronym":"FAccT '22","sponsor":["ACM Association for Computing Machinery"]},"container-title":["2022 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533224","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3531146.3533224","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3531146.3533224","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:30Z","timestamp":1750188690000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533224"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":47,"alternative-id":["10.1145\/3531146.3533224","10.1145\/3531146"],"URL":"https:\/\/doi.org\/10.1145\/3531146.3533224","relation":{},"subject":[],"published":{"date-parts":[[2022,6,20]]},"assertion":[{"value":"2022-06-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}