% Key papers for State-space and latent dynamics forecasting (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2019_time_59096,
  title = {{Time-series Generative Adversarial Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13679},
}

@inproceedings{neurips2018_deep_57014,
  title = {{Deep State Space Models for Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11747},
}

@inproceedings{aaai2017_modeling_105682,
  title = {{Modeling the Intensity Function of Point Process Via Recurrent Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10724},
}

@inproceedings{aaai2018_beyond_101289,
  title = {{Beyond Sparsity: Tree Regularization of Deep Models for Interpretability}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11501},
}

@inproceedings{icml2017_learning_36267,
  title = {{Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/757},
}

@inproceedings{icml2019_exploring_37347,
  title = {{Exploring interpretable LSTM neural networks over multi-variable data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4209},
}

@inproceedings{neurips2019_shape_58982,
  title = {{Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13576},
}

@inproceedings{neurips2020_benchmarking_59452,
  title = {{Benchmarking Deep Learning Interpretability in Time Series Predictions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18590},
}

@inproceedings{icml2018_autoregressive_36532,
  title = {{Autoregressive Convolutional Neural Networks for Asynchronous Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2310},
}

@inproceedings{neurips2017_preventing_56593,
  title = {{Preventing Gradient Explosions in Gated Recurrent Units}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8840},
}

@inproceedings{neurips2019_fully_58302,
  title = {{Fully Neural Network based Model for General Temporal Point Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13413},
}

@inproceedings{iclr2019_som_29216,
  title = {{SOM-VAE: Interpretable Discrete Representation Learning on Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/729},
}

@inproceedings{aaai2016_predicting_104711,
  title = {{Predicting ICU Mortality Risk by Grouping Temporal Trends from a Multivariate Panel of Physiologic Measurements}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/9998},
}

@inproceedings{neurips2017_yass_56778,
  title = {{YASS: Yet Another Spike Sorter}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9181},
}

@inproceedings{neurips2019_u_59127,
  title = {{U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13596},
}

@inproceedings{neurips2018_complex_56954,
  title = {{Complex Gated Recurrent Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11996},
}

@inproceedings{neurips2021_conformal_62245,
  title = {{Conformal Time-series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28166},
}

@inproceedings{icml2021_explaining_39315,
  title = {{Explaining Time Series Predictions with Dynamic Masks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8815},
}

@inproceedings{aaai2018_multi_105754,
  title = {{Multi-Step Time Series Generator for Molecular Dynamics}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11863},
}

@inproceedings{icml2019_recurrent_37687,
  title = {{Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3721},
}

@inproceedings{aaai2024_market_6604,
  title = {{Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29531},
}

@inproceedings{neurips2024_tsgm_73626,
  title = {{TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97532},
}

@inproceedings{aaai2025_neural_10103,
  title = {{Neural Conformal Control for Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34029},
}

@inproceedings{aaai2024_validation_7754,
  title = {{Validation, Robustness, and Accuracy of Perturbation-Based Sensitivity Analysis Methods for Time-Series Deep Learning Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30559},
}

@inproceedings{icml2024_timex_45571,
  title = {{TimeX++: Learning Time-Series Explanations with Information Bottleneck}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32881},
}

@inproceedings{aaai2026_time_107074,
  title = {{Time Series Forecasting via Direct Per-Step Probability Distribution Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39426},
}

@inproceedings{aaai2025_neural_10110,
  title = {{Neural Temporal Point Processes for Forecasting Directional Relations in Evolving Hypergraphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33856},
}

@inproceedings{neurips2024_shape_71547,
  title = {{Shape analysis for time series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95718},
}

@inproceedings{neurips2025_timewak_78063,
  title = {{TimeWak: Temporal Chained-Hashing Watermark for Time Series Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=akhhwQh6UV},
}

@inproceedings{neurips2025_traffident_79746,
  title = {{TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=sZvXXPqONQ},
}

@inproceedings{icml2025_lets_46988,
  title = {{LETS Forecast: Learning Embedology for Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45595},
}

@inproceedings{icml2025_privacy_47881,
  title = {{Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44722},
}

@inproceedings{icml2025_timing_48681,
  title = {{TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43941},
}

@inproceedings{icml2026_time_51637,
  title = {{Time Series Forecasting Through the Lens of Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=OL0Vx2FV1g},
}

@inproceedings{icml2026_from_54736,
  title = {{From Observations to States: Latent Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=s49fw3BVU0},
}

@inproceedings{neurips2024_soi_74155,
  title = {{SOI: Scaling Down Computational Complexity by Estimating Partial States of the Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93387},
}

@inproceedings{neurips2025_toward_76234,
  title = {{Toward Interpretable Evaluation Measures for Time Series Segmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Gz6dujD5j0},
}

@inproceedings{neurips2025_a_77844,
  title = {{A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=YAc0O13qMc},
}

@inproceedings{neurips2025_syntsbench_79635,
  title = {{SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=rNw128KrYU},
}

@inproceedings{neurips2025_oceanbench_80137,
  title = {{OceanBench: A Benchmark for Data-Driven Global Ocean Forecasting systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=wZGe1Kqs8G},
}