Jaeger H (2003) Adaptive nonlinear system identification with echo state networks. In: Advances in neural information processing systems, pp 609–616
Lukoševičius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127–149
Gallicchio C, Scardapane S (2020) Deep randomized neural networks. Recent Trends Learn Data, pp 43–68
Zhang L, Suganthan PN (2016) A survey of randomized algorithms for training neural networks. Inf Sci 364:146–155
Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667):78–80
Wu Q, Fokoue E, Kudithipudi D (2018) On the statistical challenges of echo state networks and some potential remedies. arXiv:1802.07369
Jaeger H (2005) Reservoir riddles: suggestions for echo state network research. In:Proceedings. 2005 IEEE international joint conference on neural networks, vol 3, pp 1460–1462. IEEE
Luca AT, Ulrich P (2019) Gradient based hyperparameter optimization in echo state networks. Neural Netw 115:23–29
Öztürk MM, Cankaya IA, Ipekci D (2020) Optimizing echo state network through a novel fisher maximization based stochastic gradient descent. Neurocomputing
Arrieta AB, Díaz-Rodríguez N, Del SJ, Bennetot A, Tabik S, Barbado A, Salvador G, Sergio G-L, Daniel M, Richard B, et al (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible ai. Inf Fusion 58:82–115
Gallicchio C, Micheli A, Pedrelli L (2017) Deep reservoir computing: a critical experimental analysis. Neurocomputing 268:87–99
Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural comput 14(11):2531–2560
Jaeger H (2001) The “echo state’’ approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148(34):13
Dominey PF (1995) Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning. Biol Cybern 73(3):265–274
Steil JJ (2004) Backpropagation-decorrelation: online recurrent learning with o (n) complexity. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol 2, pp 843–848. IEEE
Del S, Javier L, Ibai, M, Eric L, Oregi I, Osaba E, Lobo JL, Bilbao MN, Vlahogianni EI (2020) Deep echo state networks for short-term traffic forecasting: performance comparison and statistical assessment. In: IEEE international conference on intelligent transportation systems (ITSC), pp 1–6. IEEE
Palumbo F Gallicchio C, Pucci R, Micheli A (2016) Human activity recognition using multisensor data fusion based on reservoir computing. J Ambient Intell Smart Environ 8(2):87–107
Crisostomi E, Gallicchio C, Micheli A, Raugi M, Tucci M (2015) Prediction of the italian electricity price for smart grid applications. Neurocomputing 170:286–295
Jaeger H, Lukoševičius M, Popovici D, Siewert U (2007) Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw 20(3):335–352
Gallicchio C, Micheli A (2019) Richness of deep echo state network dynamics. In: International work-conference on artificial neural networks, pp 480–491
Gallicchio C, Micheli A (2017) Echo state property of deep reservoir computing networks. Cognit Comput 9(3):337–350
Jaeger H (2002) Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, volume 5. GMD-Forschungszentrum Informationstechnik Bonn
Gallicchio C, Micheli A, Pedrelli L (2018) Design of deep echo state networks. Neural Netw 108:33–47
Liu K, Zhang J (2020) Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy. Comput Chem Eng 135:106730
Arras L, Montavon G, Müller K-R, Samek W (2017) Explaining recurrent neural network predictions in sentiment analysis. In: Proceedings of the 8th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 159–168
Li J, Chen X, Hovy E, Jurafsky D (2016) Visualizing and understanding neural models in nlp. In: Proceedings of NAACL-HLT, pp 681–691
Denil M, Demiraj A, De Freitas N (2014) Extraction of salient sentences from labelled documents. arXiv:1412.6815
Li J, Monroe W, Jurafsky D (2016) Understanding neural networks through representation erasure. arXiv:1612.08220
Kádár A, Chrupała G, Alishahi A (2017) Representation of linguistic form and function in recurrent neural networks. Comput Linguist 43(4):761–780
Murdoch W, James L, Peter J, Yu B (2018) Beyond word importance: contextual decomposition to extract interactions from lstms. arXiv:1801.05453
Hassaballah M, Awad AI (2020) Deep learning in computer vision: principles and applications. CRC Press, Boca Raton
Rojat T, Puget R, Filliat D, Del S, Javier G, Rodolphe í-R, Natalia D (2021) Explainable artificial intelligence (xai) on time series data: a survey. arXiv:2104.00950
Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Mining Knowl Discov 15(2):107–144
Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, pp 2–11
Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. Knowl Inf Syst 3(3):263–286
Zadeh LA (1988) Fuzzy logic. Computer 21(4):83–93
Herrera F, Herrera-Viedma E, Martinez L (2000) A fusion approach for managing multi-granularity linguistic term sets in decision making. Fuzzy Sets Syst 114(1):43–58
Herrera F, Alonso S, Chiclana Francisco H-VE (2009) Computing with words in decision making: foundations, trends and prospects. Fuzzy Optim Decis Making 8(4):337–364
Mencar C, Alonso JM (2018) Paving the way to explainable artificial intelligence with fuzzy modeling. In: International Workshop on Fuzzy Logic and Applications, pp 215–227. Springer
Samek W, Montavon G, Vedaldi A, Hansen LK, Müller K-R (2019) Explainable AI: interpreting, explaining and visualizing deep learning, vol 11700. Springer
Chang Y-W, Lin C-J (2008) Feature ranking using linear svm. In: Causation and prediction challenge, pp 53–64. PMLR
Lundberg SM, Erion GG, Lee S-I (2018) Consistent individualized feature attribution for tree ensembles. arXiv:1802.03888
Smilkov D, Thorat N, Kim B, Viégas F, Wattenberg M (2017) Smoothgrad: removing noise by adding noise. arXiv:1706.03825
Adebayo J, Gilmer J, Muelly M, Goodfellow I, Hardt M, Kim B (2018) Sanity checks for saliency maps. arXiv:1810.03292
Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2014) Striving for simplicity: the all convolutional net. arXiv:1412.6806
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626
Montavon G, Lapuschkin S, Binder A, Samek W, Müller K-R (2017) Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recognit 65:211–222
Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv:1312.6034
Ancona M, Ceolini E, Öztireli C, Gross M (2017) Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv:1711.06104
Baehrens D, Schroeter T, Harmeling S, Kawanabe M, Hansen K, Müller K-R (2010) How to explain individual classification decisions. J Mach Learn Res 11:1803–1831
Shrikumar A, Greenside P, Kundaje A (2017) Learning important features through propagating activation differences. In: International conference on machine learning, pp 3145–3153. PMLR
Ribeiro MT, Singh S, Guestrin C (2016) “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135–1144
Marwan N, Romano MC, Thiel M, Kurths J (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438(5–6):237–329
Eckmann J-P, Kamphorst SO, Ruelle D, et al (1995) Recurrence plots of dynamical systems. World Sci Ser Nonlinear Sci Ser A 16:441–446
Gallicchio C, Micheli A (2016) Deep reservoir computing: a critical analysis. In: ESANN
Schaetti N, Salomon M, Couturier R (2016) Echo state networks-based reservoir computing for mnist handwritten digits recognition. In: IEEE international conference on computational science and engineering (CSE), pp 484–491. IEEE
Woodward A, Ikegami T (2011) A reservoir computing approach to image classification using coupled echo state and back-propagation neural networks. In International conference image and vision computing, Auckland, New Zealand, pp 543–458
Souahlia A, Belatreche A, Benyettou A, Curran K (2016) An experimental evaluation of echo state network for colour image segmentation. In: 2016 International joint conference on neural networks (IJCNN), pp 1143–1150. IEEE
Tong Z, Tanaka G (2018) Reservoir computing with untrained convolutional neural networks for image recognition. In: International conference on pattern recognition (ICPR), pp 1289–1294. IEEE
Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-K, Woo W-C (2015) Convolutional lstm network: a machine learning approach for precipitation nowcasting. arXiv:1506.04214
Laña I, Del SJ, Padró A, Vélez M, Casanova-Mateo C (2016) The role of local urban traffic and meteorological conditions in air pollution: a data-based case study in Madrid. Spain. Atmos Environ 145:424–438
Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local svm approach. In: Proceedings of the 17th International conference on pattern recognition, 2004. ICPR 2004., volume 3, pp 32–36. IEEE
Blank M, Gorelick L, Shechtman E, Irani M, Basri R (2005) Actions as space-time shapes. In: Tenth IEEE international conference on computer vision (ICCV’05) Volume 1, volume 2, pp 1395–1402. IEEE
Weinland D, Ronfard R, Boyer E (2006) Free viewpoint action recognition using motion history volumes. Comput Vis Image Understand 104(2–3):249–257
Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos “in the wild”. In: 2009 IEEE conference on computer vision and pattern recognition, pp 1996–2003. IEEE
Reddy KK, Shah M (2013) Recognizing 50 human action categories of web videos. Mach Vis Appl 24(5):971–981
Soomro K, Zamir AR, Shah M: Ucf101: a dataset of 101 human actions classes from videos in the wild. arXiv:1212.0402
Rodriguez MD, Ahmed J, Shah M (2008) Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: IEEE conference on computer vision and pattern recognition, pp 1–8. IEEE
Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T (2011) Hmdb: a large video database for human motion recognition. In: 2011 International conference on computer vision, pp 2556–2563. IEEE
LeCun Y (1998) The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/
Han D, Bo L, Sminchisescu C (2009) Selection and context for action recognition. In: 2009 IEEE 12th international conference on computer vision, pp 1933–1940
Ghadiyaram D, Tran D, Mahajan D (2019) Large-scale weakly-supervised pre-training for video action recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12046–12055
Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A (2011) Sequential deep learning for human action recognition. In: International workshop on human behavior understanding, pp 29–39. Springer
Shu Na, Tang Q, Liu H (2014) A bio-inspired approach modeling spiking neural networks of visual cortex for human action recognition. In: 2014 international joint conference on neural networks (IJCNN), pp 3450–3457. IEEE
Liu J, Shah M (2008) Learning human actions via information maximization. In: IEEE conference on computer vision and pattern recognition, pp 1–8. IEEE
Sharma S, Kiros R, Salakhutdinov R (2015) Action recognition using visual attention. (2015). arXiv:1511.04119
Shi Y, Zeng W, Huang T, Wang Y (2015) Learning deep trajectory descriptor for action recognition in videos using deep neural networks. In: 2015 IEEE international conference on multimedia and expo (ICME), pp 1–6. IEEE
Wang L, Qiao Y, Tang X (2015) Action recognition with trajectory-pooled deep-convolutional descriptors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4305–4314
Harandi MT, Sanderson C, Shirazi S, Lovell BC (2013) Kernel analysis on grassmann manifolds for action recognition. Pattern Recognit Lett 34(15):1906–1915