Record · danmackinlay.name

Recurrent neural networks – The Dan MacKinlay stable of variably-well-consider’d enterprises

Dan MacKinlay · 1 October 2026 · 9 min read

A post on danmackinlay.name, published 1 October 2026, has not been cited by any blog yet (corpus.blog, measured 12 October 2026).

A record is what was published and who pointed at it. The text of the post is not shown here: read it at the source, or at its Wayback capture.

Read at danmackinlay.name Wayback capture

0blogs citing

Citing blogs and links by period
Period	Blogs	Links
All time	0	0
Last 90 days	0	0
Last 30 days	0	0
Last 7 days	0	0

Figures for this page
Words blogs link it with	0
External links	74
Length	2,069

Cited by

No blog we hold has cited this post yet.

Links from this post

Link

Host

https://github.com/kjw0612/awesome-rnn?ref=danmackinlay.name

github.com

http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Who cites this page

karpathy.github.io

Linklog | Understanding LSTM Networks Read at colah.github.io

colah.github.io

http://jeffdonahue.com/lrcn/?ref=danmackinlay.name

jeffdonahue.com

http://deeplearning.net/tutorial/lstm.html?ref=danmackinlay.name

deeplearning.net

https://pytorch-forecasting.readthedocs.io/en/latest/getting-started.html?ref=danmackinlay.name

pytorch-forecasting.readthedocs.io

https://pytorch-forecasting.readthedocs.io/en/latest/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html?ref=danmackinlay.name

pytorch-forecasting.readthedocs.io

https://tianyi-zhang.github.io/files/chi2023-deepseer.pdf?ref=danmackinlay.name

tianyi-zhang.github.io

https://github.com/momentum-lab-workspace/DeepSeer?ref=danmackinlay.name

github.com

https://proceedings.mlr.press/v115/aicher20a.html

proceedings.mlr.press

https://arxiv.org/abs/1511.06464 Who cites this page

arxiv.org

https://doi.org/10.1109/TNNLS.2015.2411629

doi.org

https://doi.org/10.1109/72.279181 Who cites this page

doi.org

http://papers.nips.cc/paper/5956-scheduled-sampling-for-sequence-prediction-with-recurrent-neural-networks Who cites this page

papers.nips.cc

http://link.springer.com/chapter/10.1007/11732242_62

link.springer.com

https://doi.org/10.1111/j.1467-6419.2007.00518.x

doi.org

http://arxiv.org/abs/1409.1259

arxiv.org

http://arxiv.org/abs/1609.01704

arxiv.org

http://arxiv.org/abs/1502.02367

arxiv.org

http://papers.nips.cc/paper/5653-a-recurrent-latent-variable-model-for-sequential-data.pdf

papers.nips.cc

https://proceedings.neurips.cc/paper_files/paper/2021/hash/ef452c63f81d0105dd4486f775adec81-Abstract.html

proceedings.neurips.cc

https://arxiv.org/abs/1611.09913 Who cites this page

arxiv.org

https://arxiv.org/abs/1603.09025

arxiv.org

https://doi.org/10.1073/pnas.1508431112

doi.org

https://doi.org/10.1016/0364-0213(90

doi.org

http://arxiv.org/abs/1704.02798

arxiv.org

http://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf

papers.nips.cc

https://doi.org/10.1162/089976600300015015 Who cites this page

doi.org

http://www.jmlr.org/papers/v3/gers02a.html

jmlr.org

https://papers.nips.cc/paper/4329-practical-variational-inference-for-neural-networks.pdf

papers.nips.cc

https://arxiv.org/abs/1502.04623 Who cites this page

arxiv.org

http://papers.nips.cc/paper/6221-memory-efficient-backpropagation-through-time.pdf

papers.nips.cc

https://doi.org/10.1109/IJCNN.2009.5178822

doi.org

http://arxiv.org/abs/1609.05191

arxiv.org

https://doi.org/10.1016/j.eswa.2011.06.052

doi.org

http://arxiv.org/abs/1606.04801

arxiv.org

https://doi.org/10.1142/S0218488598000094

doi.org

http://www.bioinf.jku.at/publications/older/ch7.pdf

bioinf.jku.at

https://papers.nips.cc/paper/1215-lstm-can-solve-hard-long-time-lag-problems.pdf

papers.nips.cc

https://doi.org/10.1162/neco.1997.9.8.1735 Who cites this page

doi.org

http://machinelearning.wustl.edu/mlpapers/paper_files/icml2015_jozefowicz15.pdf

machinelearning.wustl.edu

https://arxiv.org/abs/1506.02078 Who cites this page

arxiv.org

https://arxiv.org/abs/1606.04934 Who cites this page

arxiv.org

http://arxiv.org/abs/1402.3511

arxiv.org

https://doi.org/10.1103/physrevx.12.011011

doi.org

https://arxiv.org/abs/1511.05121

arxiv.org

http://arxiv.org/abs/1610.09038

arxiv.org

http://arxiv.org/abs/1612.06212

arxiv.org

https://doi.org/10.1016/j.conb.2019.01.011

doi.org

http://www.igi.tu-graz.ac.at/maass/psfiles/149-v05.pdf

igi.tu-graz.ac.at

http://arxiv.org/abs/1810.10999

arxiv.org

https://arxiv.org/abs/1705.09279

arxiv.org

http://www.cs.utoronto.ca/~jmartens/docs/Deep_HessianFree.pdf

cs.utoronto.ca

http://dl.acm.org/citation.cfm?id=3104482.3104612

dl.acm.org

http://www.cs.toronto.edu/~jmartens/docs/HF_book_chapter.pdf

cs.toronto.edu

https://doi.org/10.21437/Interspeech.2010-343

doi.org

http://arxiv.org/abs/1805.10369

arxiv.org

https://doi.org/10.1109/TASL.2011.2109382

doi.org

http://arxiv.org/abs/1904.12933

arxiv.org

http://arxiv.org/abs/1703.00381

arxiv.org

http://arxiv.org/abs/1211.5063 Who cites this page

arxiv.org

http://arxiv.org/abs/1511.06309

arxiv.org

http://arxiv.org/abs/1611.04500

arxiv.org

http://arxiv.org/abs/1506.01698

arxiv.org

https://doi.org/10.1016/0005-1098(95

doi.org

https://doi.org/10.1103/PhysRevLett.61.259

doi.org

https://doi.org/10.1109/IJCNN.2004.1380039

doi.org

http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_693.pdf

machinelearning.wustl.edu

http://arxiv.org/abs/1506.03478

arxiv.org

http://arxiv.org/abs/1711.11053

arxiv.org

http://papers.nips.cc/paper/6327-full-capacity-unitary-recurrent-neural-networks

papers.nips.cc

http://arxiv.org/abs/1611.07252

arxiv.org

http://papers.nips.cc/paper/6215-on-multiplicative-integration-with-recurrent-neural-networks.pdf

papers.nips.cc

http://arxiv.org/abs/1502.08029

arxiv.org

Elsewhere on danmackinlay.name (14)

Neural nets – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

State filtering for hidden Markov models – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Recurrent / convolutional / state-space – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Differentiable learning of automata – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Neural learning dynamical systems – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Here’s how I would do art with machine learning if I had to – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Feedback system identification, linear – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Stability in linear dynamical systems – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Delays and reverbs for audio processing – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Orthonormal and unitary matrices – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Transformer networks – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Reservoir Computing – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Recursive identification – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Tabular data processing in Python – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Recurrent neural networks – The Dan MacKinlay stable of variably-well-consider’d enterprises

Cited by

Links from this post

Similar posts