The DOI matches its Crossref record; 17 of the 19 references it cites were found in a registry or an index.
Multi-dimensional Recurrent Neural Networks
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In the citing paper
“the limitations in simultaneously capturing the evolving patterns and dependencies of MTS due to the restriction of each instance's hypothesis space to its individual inputs [22,23]”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [23]
In the citing paper
“Although similar in concept, our proposed DS-LSTM has a distinct architecture, and is designed to process two sequences of one-dimensional data instead of multi-dimensional data.”
From Speech Emotion Recognition with Dual-Sequence LSTM Architecture (2020) · cites this paper as [11]
In the citing paper
“It should be noted that previous research in multi-dimensional LSTM (MD-LSTM) [11, 12, 13], especially in ASR [14, 15], focused on adapting the LSTM to a multi-dimensional data format.”
From Speech Emotion Recognition with Dual-Sequence LSTM Architecture (2020) · cites this paper as [11]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Self-citationGradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
Self-citationGraves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370
Self-citationBest practices for convolutional neural networks applied to visual document analysis
Simard, P. Y., Steinkraus, D., & Platt, J. C. (2005) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2003.1227801 · Cited by 2,862
Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies
(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544037.ch14 · Cited by 1,312
Bridle, J. S. (1990) · Springer eBooks · doi:10.1007/978-3-642-76153-9_28 · Cited by 1,278
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition
Graves, A., Fernández, S., & Schmidhuber, J. (2005) · Lecture Notes in Computer Science · doi:10.1007/11550907_126 · Cited by 925
Self-citationBackpropagation: Theory, architectures, and applications
Biron, P. V. (1997) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199701)48:1 · Cited by 620
Gradient-based learning algorithms for recurrent networks and their computational complexity
Williams, R. J., & Zipser, D. (1995) · Cited by 413
Image classification by a two-dimensional hidden Markov model
Li, J., Najmi, A., & Gray, R. M. (2000) · IEEE Transactions on Signal Processing · doi:10.1109/78.823977 · Cited by 293
Liwicki, M., Graves, A., Horst Bunke, A., & Schmidhuber, J. (2007) · BORIS (University Library Bern) · Cited by 183
Self-citationProceedings of the 23rd DAGM-Symposium on Pattern Recognition
Radig, B., & Florczyk, S. (2001) · Cited by 16
Facial Expression Recognition with Pseudo-3D Hidden Markov Models
Hülsken, F., Wallhoff, F., & Rigoll, G. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45404-7_39 · Cited by 10
Multi-Dimensional Dependency-Tree Hidden Markov Models
Mérialdo, B., Jitén, J., & Huet, B. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1660457 · Cited by 4
Parameter estimation of multi-dimensional hidden Markov models - a scalable approach
Joshi, D., Li, J., & Wang, J. Z. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530350 · Cited by 2
Research works printed but not linked: 1
- Gers, F., Schraudolph, N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research 3, 115–143 (2002) · DOI
Cited by
Sorted by citations
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019) · Neural Computation · doi:10.1162/neco_a_01199 · Cited by 5,870
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
Self-citationMachine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063
A review on the long short-term memory model
Houdt, G. V., Mosquera, C., & Nápoles, G. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09838-1 · Cited by 2,198
A survey on deep learning techniques for image and video semantic segmentation
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., Martínez-González, P., & García‐Rodríguez, J. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.018 · Cited by 2,646
Metadata
- Authors
- Graves, A., Fernández, S., & Schmidhuber, J.
- Affiliations
- Dalle Molle Institute for Artificial Intelligence Research (CH)
- ORCID
- Schmidhuber, Jürgen
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2007
- Pages
- 549-558
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1685006559
- Cited by
- 303
- References
- 19
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 20 · 2023: 23 · 2022: 13 · 2021: 30 · 2020: 29 · 2019: 28 · 2018: 36 · 2017: 25 · 2016: 24 · 2015: 16 · 2014: 10 · 2013: 6 · 2012: 7
- FWCI
- 3.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- recurrent neural networks, spatio-temporal data, image segmentation, video processing, diagnostic imaging, contextual information
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