% Key papers for LSTM design and memory (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2016_siamese_105196,
  title = {{Siamese Recurrent Architectures for Learning Sentence Similarity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10350},
}

@inproceedings{aaai2018_targeted_106006,
  title = {{Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12048},
}

@inproceedings{iclr2018_regularizing_28650,
  title = {{Regularizing and Optimizing LSTM Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/316},
}

@inproceedings{cvpr2020_high_15652,
  title = {{High-Performance Long-Term Tracking With Meta-Updater}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2017_transfer_28247,
  title = {{Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=ByxpMd9lx},
}

@inproceedings{cvpr2017_cern_12098,
  title = {{CERN: Confidence-Energy Recurrent Network for Group Activity Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{aaai2016_learning_105193,
  title = {{Learning Statistical Scripts with LSTM Recurrent Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10347},
}

@inproceedings{cvpr2017_recurrent_12574,
  title = {{Recurrent Modeling of Interaction Context for Collective Activity Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{iclr2017_tracking_28363,
  title = {{Tracking the World State with Recurrent Entity Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=rJTKKKqeg},
}

@inproceedings{aaai2016_listen_105210,
  title = {{Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10364},
}

@inproceedings{iclr2018_learning_28686,
  title = {{Learning Intrinsic Sparse Structures within Long Short-Term Memory}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/337},
}

@inproceedings{icml2016_associative_35742,
  title = {{Associative Long Short-Term Memory}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/danihelka16.html},
}

@inproceedings{neurips2019_legendre_58541,
  title = {{Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14639},
}

@inproceedings{iclr2017_automatic_28318,
  title = {{Automatic Rule Extraction from Long Short Term Memory Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=SJvYgH9xe},
}

@inproceedings{aaai2019_towards_101509,
  title = {{Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4200},
}

@inproceedings{neurips2017_wider_56776,
  title = {{Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8798},
}

@inproceedings{aaai2016_morphological_105209,
  title = {{Morphological Segmentation with Window LSTM Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10363},
}

@inproceedings{icml2018_towards_37053,
  title = {{Towards Binary-Valued Gates for Robust LSTM Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1916},
}

@inproceedings{icml2020_do_38121,
  title = {{Do RNN and LSTM have Long Memory?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5907},
}

@inproceedings{aaai2016_agreement_105173,
  title = {{Agreement on Target-Bidirectional LSTMs for Sequence-to-Sequence Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10327},
}

@inproceedings{aaai2025_extended_9093,
  title = {{Extended LSTMs for Knowledge Tracing: Peeking Inside the Black Box (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35312},
}

@inproceedings{iclr2025_tuning_83998,
  title = {{Tuning Frequency Bias of State Space Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27827},
}

@inproceedings{icml2024_parallelized_44126,
  title = {{Parallelized Spatiotemporal Slot Binding for Videos}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34326},
}

@inproceedings{icml2024_stablessm_45325,
  title = {{StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33127},
}

@inproceedings{icml2024_memoria_45784,
  title = {{Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32668},
}

@inproceedings{neurips2025_efficient_78437,
  title = {{Efficient Allocation of Working Memory Resource for Utility Maximization in Humans and Recurrent Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=eYVFs6TfQ0},
}

@inproceedings{iclr2025_hope_82146,
  title = {{HOPE for a Robust Parameterization of Long-memory State Space Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29646},
}

@inproceedings{neurips2026_prospective_92781,
  title = {{Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153925},
}

@inproceedings{neurips2026_linear_94842,
  title = {{Linear approximations to HMM filtering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155014},
}