The DOI matches its Crossref record.
Supervised Sequence Labelling
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Citations
Cited by
Learning Text Similarity with Siamese Recurrent Networks
Neculoiu, P., Versteegh, M., & Rotaru, M. (2016) · Proceedings of the 1st Workshop on Representation Learning for NLP · doi:10.18653/v1/w16-1617 · Cited by 341
“…and below the logistic function is denoted by σ The Long Short-Term Memory (Hochreiter and Schmidhuber, 1997) variant of RNNs in particular has had success in tasks related to natural language processing, such as text classification (Graves, 2012) and language translation (Sutskever et al., 2014).”
BackgroundDiPietro, R., & Hager, G. D. (2020) · Handbook of Medical Image Computing and Computer Assisted Intervention · doi:10.1016/b978-0-12-816176-0.00026-0 · Cited by 293
Chen, H., Dou, Q., Ni, D., Cheng, J.-Z., Qin, J., Li, S., & Heng, P.-A. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_62 · Cited by 180
Geospatial data to images: a deep-learning framework for traffic forecasting
Jiang, W., & Zhang, L. (2019) · Tsinghua Science and Technology · doi:10.26599/tst.2018.9010033 · Cited by 176
Data-driven subtyping of Parkinson's disease using longitudinal clinical records: a cohort study
Zhang, X., Chou, J., Liang, J., Xiao, C., Zhao, Y., Sarva, H., Henchcliffe, C., & Wang, F. (2019) · Scientific Reports · doi:10.1038/s41598-018-37545-z · Cited by 137
Automated annotation of birdsong with a neural network that segments spectrograms
Cohen, Y., Nicholson, D. A., Sanchioni, A., Mallaber, E. K., Skidanova, V., & Gardner, T. J. (2022) · eLife · doi:10.7554/elife.63853 · Cited by 80
Multi-Modality Multi-Task Recurrent Neural Network for Online Action Detection
Liu, J., Li, Y., Song, S., Xing, J., Lan, C., & Zeng, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799968 · Cited by 62
Prediction of gait trajectories based on the Long Short Term Memory neural networks
Zaroug, A., Garofolini, A., Lai, D. T. H., Mudie, K., & Begg, R. (2021) · PLOS ONE · doi:10.1371/journal.pone.0255597 · Cited by 48
Meta-learning of Sequential Strategies
Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv · doi:10.48550/arxiv.1905.03030 · Cited by 47
Metadata
- Authors
- Graves, A.
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 5-13
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W950853366
- Cited by
- 545
- References
- 0
- FWCI
- 5.18
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.