% Key papers for Dynamical system reconstruction (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2020_hippo_59998,
  title = {{HiPPO: Recurrent Memory with Optimal Polynomial Projections}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17173},
}

@inproceedings{aaai2023_causal_3245,
  title = {{Causal Recurrent Variational Autoencoder for Medical Time Series Generation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26031},
}

@inproceedings{icml2016_efficient_35814,
  title = {{Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/guan16.html},
}

@inproceedings{cvpr2021_affect2mm_16588,
  title = {{Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2020_deep_59673,
  title = {{Deep reconstruction of strange attractors from time series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16951},
}

@inproceedings{icml2020_forecasting_38223,
  title = {{Forecasting Sequential Data Using Consistent Koopman Autoencoders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6182},
}

@inproceedings{neurips2016_interpretable_55796,
  title = {{Interpretable Nonlinear Dynamic Modeling of Neural Trajectories}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7130},
}

@inproceedings{aaai2020_factorized_103345,
  title = {{Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6597},
}

@inproceedings{neurips2021_disentangling_61336,
  title = {{Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27606},
}

@inproceedings{neurips2020_latent_60143,
  title = {{Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17732},
}

@inproceedings{iclr2021_identifying_30432,
  title = {{Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3338},
}

@inproceedings{neurips2021_machine_63146,
  title = {{Machine learning structure preserving brackets for forecasting irreversible processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28550},
}

@inproceedings{iclr2019_learning_28845,
  title = {{Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1057},
}

@inproceedings{icml2022_reconstructing_41069,
  title = {{Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17181},
}

@inproceedings{neurips2019_learning_58521,
  title = {{Learning low-dimensional state embeddings and metastable clusters from time series data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13608},
}

@inproceedings{neurips2019_unsupervised_59153,
  title = {{Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14349},
}

@inproceedings{icml2018_probabilistic_36932,
  title = {{Probabilistic Recurrent State-Space Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2070},
}

@inproceedings{aaai2019_on_101608,
  title = {{On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4307},
}

@inproceedings{icml2016_the_36008,
  title = {{The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/saeedi16.html},
}

@inproceedings{icml2020_learnable_38366,
  title = {{Learnable Group Transform For Time-Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6701},
}

@inproceedings{aaai2024_learning_6500,
  title = {{Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29406},
}

@inproceedings{icml2024_integrating_44794,
  title = {{Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33658},
}

@inproceedings{aaai2024_pac_6886,
  title = {{PAC-Bayes Generalisation Bounds for Dynamical Systems including Stable RNNs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29076},
}

@inproceedings{aaai2024_sequential_7323,
  title = {{Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30439},
}

@inproceedings{aaai2025_erci_8877,
  title = {{ERCI: An Explainable Experience Replay Approach with Causal Inference for Deep Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34981},
}

@inproceedings{neurips2024_a_73198,
  title = {{A scalable generative model for dynamical system reconstruction from neuroimaging data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94236},
}

@inproceedings{neurips2024_learning_74618,
  title = {{Learning diverse causally emergent representations from time series data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92973},
}

@inproceedings{iclr2025_zapbench_83444,
  title = {{ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28372},
}

@inproceedings{aaai2025_modeling_9982,
  title = {{Modeling Latent Non-Linear Dynamical System over Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33269},
}

@inproceedings{neurips2024_towards_71467,
  title = {{Towards Understanding Evolving Patterns in Sequential Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95792},
}

@inproceedings{neurips2024_inference_72034,
  title = {{Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95284},
}

@inproceedings{iclr2025_oscillatory_81498,
  title = {{Oscillatory State-Space Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30278},
}

@inproceedings{iclr2025_learning_82395,
  title = {{Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29398},
}

@inproceedings{neurips2026_ramanbench_92169,
  title = {{RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/139729},
}

@inproceedings{iclr2024_let_34022,
  title = {{Let's do the time-warp-attend: Learning topological invariants of dynamical systems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19053},
}

@inproceedings{iclr2024_efficient_34155,
  title = {{Efficient Inverse Multiagent Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18920},
}

@inproceedings{icml2024_from_44597,
  title = {{From Generalization Analysis to Optimization Designs for State Space Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33855},
}

@inproceedings{icml2024_consistent_44911,
  title = {{Consistent Long-Term Forecasting of Ergodic Dynamical Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33541},
}

@inproceedings{icml2025_skolr_47648,
  title = {{SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44949},
}

@inproceedings{icml2026_pulse_51163,
  title = {{PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=JJIqZzujgE},
}