Multi-dimensional Recurrent Neural Networks
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Multiple-instance configurations limit simultaneously capturing evolving patterns and dependencies of MTS.
“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]
The proposed DS-LSTM has a distinct architecture from previous MD-LSTM and is designed to process two sequences of one-dimensional data instead of multi-dimensional data.
“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]
Previous research in multi-dimensional LSTM (MD-LSTM), especially in ASR, focused on adapting the LSTM to a multi-dimensional data format.
“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]
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- 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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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.
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