Who Cited It

Learning long-term dependencies with gradient descent is difficult

1994 · IEEE Transactions on Neural Networks · 8,610 citations · 24 from inside this corpus

Yoshua Bengio, P. Simard, Paolo Frasconi

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Learning long-term dependencies with gradient descent is difficult (1994)Learning long-term dependenci…Learning Internal Representations by Error Propagation (1985)Learning Internal Representat…Parallel Distributed Processing (1986)Parallel Distributed Processi…A Learning Algorithm for Continually Running Fully Recurrent Neural Networks (1989)A Learning Algorithm for Cont…Advances in Neural Information Processing Systems 37 (2024)Advances in Neural Informatio…Advances in Neural Information Processing Systems 5 (1993)Advances in Neural Informatio…Long Short-Term Memory (1997)Long Short-Term MemoryDeep learning in neural networks: An overview (2014)Deep learning in neural netwo…A survey on deep learning in medical image analysis (2017)A survey on deep learning in …Sequence to Sequence Learning with Neural Networks (2014)Sequence to Sequence Learning…Understanding the difficulty of training deep feedforward neural networks (2010)Understanding the difficulty …A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures (2019)A Review of Recurrent Neural …Learning to Forget: Continual Prediction with LSTM (2000)Recurrent neural network based language model (2010)Recurrent neural network base…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Neural Architectures for Named Entity Recognition (2016)Neural Architectures for Name…On the difficulty of training Recurrent Neural Networks (2012)On the difficulty of training…Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017)Deep Convolutional Neural Net…On the importance of initialization and momentum in deep learning (2013)On the importance of initiali…Reservoir computing approaches to recurrent neural network training (2009)Reservoir computing approache…Long short-term memory recurrent neural network architectures for large scale acoustic mo… (2014)Long short-term memory recurr…End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF (2016)End-to-end Sequence Labeling …Conditional Random Fields as Recurrent Neural Networks (2015)Conditional Random Fields as …Deep Learning for Health Informatics (2016)Deep Learning for Health Info…LSTM neural networks for language modeling (2012)LSTM neural networks for lang…Deep learning for sentiment analysis: A survey (2018)Deep learning for sentiment a…Deep Learning: Methods and Applications (2014)Extensions of recurrent neural network language model (2011)Extensions of recurrent neura…Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (2015)Document Modeling with Gated …An Empirical Exploration of Recurrent Network Architectures (2015)An Empirical Exploration of R…
29 of 29 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Learning Internal Representations by Error Propagation198516,273
Parallel Distributed Processing198615,411
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks19894,490
Advances in Neural Information Processing Systems 3720242,802
Advances in Neural Information Processing Systems 519931,857

What cites it, inside the corpus

PaperYearCited
Long Short-Term Memory1997101,359
Deep learning in neural networks: An overview201418,236
A survey on deep learning in medical image analysis201715,110
Sequence to Sequence Learning with Neural Networks201413,351
Understanding the difficulty of training deep feedforward neural networks201012,673
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures20195,628
Learning to Forget: Continual Prediction with LSTM20005,538
Recurrent neural network based language model20105,432
Learning Deep Architectures for AI20095,077
Neural Architectures for Named Entity Recognition20164,482
On the difficulty of training Recurrent Neural Networks20123,801
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review20173,570
On the importance of initialization and momentum in deep learning20133,523
Reservoir computing approaches to recurrent neural network training20092,992
Long short-term memory recurrent neural network architectures for large scale acoustic mo…20142,986
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF20162,595
Conditional Random Fields as Recurrent Neural Networks20152,437
Deep Learning for Health Informatics20162,015
LSTM neural networks for language modeling20121,996
Deep learning for sentiment analysis: A survey20181,918
Deep Learning: Methods and Applications20141,801
Extensions of recurrent neural network language model20111,603
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification20151,548
An Empirical Exploration of Recurrent Network Architectures20151,406

Links

DOI · OpenAlex record

Topics

Neural Networks and ApplicationsComputer Science
Domain Adaptation and Few-Shot LearningComputer Science
Machine Learning and Data ClassificationComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports3 author record(s) attached.
  • supports26 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:43+00:00.

sha256 5cad55ac5d4d41e1…