Pith. sign in

REVIEW 3 major objections 5 minor 69 references

MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecasting

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read MissHyper claims that restoring co-timestamp context before hypergraph propagation improves sparse clinical forecasting, and shows consistent MSE and MAE reductions across three ICU benchmarks.

desk verdict A clear, well-described pre-propagation module for clinical hypergraph forecasting; the gains are consistent but not yet causally isolated from the added capacity. read the letter →

arxiv 2607.21922 v1 pith:IFCYXGIL submitted 2026-07-24 cs.LG

Mingyi Ma , Qingxiong Tan This is my paper · ORCID
classification cs.LG
keywords irregularmultivariatetimeseriesclinicalforecastingmissingnesshypergraphneuralnetworkseventinitializationco-timestampcontextsynchronicityrestorationICUbenchmarks
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

MissHyper argues that event-centric models for sparse clinical time series flatten co-timestamp measurements too early: records taken at the same timestamp are embedded as independent nodes, so the model must rediscover their shared snapshot through later message passing. The paper proposes restoring that snapshot before propagation begins, by adding a support-density cue, summarizing same-timestamp embeddings, and gating how much each node borrows from the summary. On three ICU benchmarks this initialization module reduces both MSE and MAE relative to the same hypergraph backbone with unchanged propagation, with the clearest gains on the sparsest high-dimensional dataset. The claim is that event initialization, not just dependency propagation, is a critical design axis for sparse clinical forecasting.

What carries the argument

The load-bearing object is the timestamp-level context restoration combined with the missingness-guided gate. For each event node, a support-density scalar is computed by averaging the availability mask over a local time window, giving a bounded cue of how well the measurement is surrounded by other records. All observed events sharing a timestamp are then pooled into a support-weighted context vector, and a sigmoid gate, conditioned on the node embedding, the context, and the density, interpolates between node-specific evidence and the restored snapshot. This operation is deliberately not another message-passing layer: it only rewrites the initial node states that enter the unchanged hypergraph backbone, so the comparison isolates the effect of better event initialization.

What would settle it

One decisive test is to shuffle the timestamp assignments of co-occurring measurements while preserving each event's time and value marginals, so that same-timestamp co-occurrence becomes pure noise; if MissHyper's gains over the same backbone persist, the claimed mechanism is not restoring real snapshot context. A complementary check is a synthetic dataset with known same-timestamp coupling strength, where the method's gain should scale with the coupling if the premise is right.

Watch

Extended reading notes

Core claim

The paper's central discovery is a pre-propagation representation bottleneck: in sparse event-centric forecasting, nodes sharing a timestamp are initialized from local features alone, even though they jointly describe a patient-state snapshot. MissHyper removes this bottleneck by computing, for each timestamp, a support-weighted average of the embeddings of observed events at that timestamp, projecting it, and fusing it into each node's embedding through a missingness-guided dimension-wise gate. Because the context is computed from observed events only and query values are excluded, the restoration adds no target leakage. The experiments show that this lightweight encoder change, with the hypergraph propagation backbone held fixed, lowers MSE from 0.3010 to 0.2962 on PhysioNet 2012, from 0.4009 to 0.3860 on MIMIC-III, and from 0.2136 to 0.2081 on MIMIC-IV, with parallel MAE reductions; ablations attribute the gain to the combination of snapshot restoration, adaptive gating, and the support-density cue.

Load-bearing premise

The load-bearing premise is that measurements sharing a timestamp form a coherent patient-state snapshot that is best summarized by a support-weighted average of their embeddings before any learned propagation; if same-timestamp co-occurrence mostly reflects documentation timing rather than physiological coupling, or if averaging embeddings across variables with different scales destroys information the gate cannot recover, the reported gains would not generalize.

Editorial extensions

If this is right

  • Any event-centric forecasting architecture can adopt the same pre-propagation restoration without redesigning its downstream propagation, since the module only rewrites initial node embeddings.
  • The benefit should be largest where sparsity is high and co-timestamp measurements are rare, as observed on MIMIC-III, suggesting initialization improvements matter most in high-dimensional sparse regimes.
  • Missingness-derived support density is a useful reliability cue beyond value and role indicators; removing it consistently hurts performance.
  • The restored context is safe to compute from observed events only, so query target values never leak into the aggregation, making the module compatible with multi-step forecasting objectives.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension is to wrap the same snapshot-restoration and gating module around non-hypergraph event models such as attention- or set-based forecasters; if the bottleneck is general, the gains should transfer without changing those backbones.
  • Because support weighting averages embeddings from different clinical variables, the gate may need input normalization or variable-type conditioning when applied to panels mixing vitals and laboratory values; this is an untested boundary of the paper's setup.
  • One could construct a diagnostic where timestamps are randomly rounded to create artificial co-occurrences; if MissHyper then improves over the baseline, the improvement would be attributable to layout regularization rather than true clinical synchronicity, separating the mechanism from the clinical story.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes MissHyper, an initialization module for hypergraph-based forecasting of irregular multivariate clinical time series. It augments each event node with a support-density cue, aggregates same-timestamp event embeddings into a timestamp context, and adaptively fuses that context via a missingness-guided gate before propagation through an unchanged HyperIMTS backbone. Experiments on PhysioNet 2012, MIMIC-III, and MIMIC-IV report consistent MSE and MAE reductions over HyperIMTS and several other baselines, with ablations suggesting that snapshot restoration, adaptive fusion, and support-density encoding all contribute. The manuscript's central claim is that restoring co-timestamp context before message passing improves sparse clinical forecasting.

Significance. If the attribution were established, the finding would be useful: it identifies a cheap, architecture-agnostic initialization principle and evaluates it on three standard benchmarks with a controlled backbone. Strengths include the clean experimental isolation of the encoder-side change, validation-based selection of the only extra hyperparameter, five-seed reporting, and no circularity in evaluation because test splits are external and held out. The method equations are coherent and the paper is clearly written. However, the experimental evidence does not yet uniquely pin the reported gains to synchronicity restoration: the full model adds learned capacity beyond the backbone, and the principal ablation still beats the baseline on every dataset, so the central attribution needs additional control experiments and statistical support.

major comments (3)
  1. [Section 4.3, Table 3] The attribution of the Table 2 gains to pre-propagation synchronicity restoration is confounded by added capacity. The 'w/o Snapshot Restoration' variant still includes the support-density input and the gated-fusion projection (Section 3.4) and still outperforms HyperIMTS on every dataset (P12 0.2984 vs 0.3010; MIMIC-III 0.3942 vs 0.4009; MIMIC-IV 0.2110 vs 0.2136). Since Section 3.5 states that the backbone already has timestamp hyperedges, and since MissHyper adds three learned components (the rho input dimension, phi_c in Section 3.3, and phi_g in Section 3.4), the comparison conflates synchronicity restoration with extra model capacity. Please add a capacity-matched control, for example a per-node MLP inside HyperIMTS with the same parameter budget, or a variant in which the context is replaced by shuffled or content-free co-timestamp aggregates while phi_c and phi_g are retained; the synchronicity-specific claim must survive such a control.
  2. [Section 4.2, Table 2; Section 4.3, Table 3] No statistical significance tests are reported for the central comparison or for the ablations. With five seeds per configuration, some reported differences are not obviously robust: under a naive two-sample t-test the MIMIC-III HyperIMTS-vs-MissHyper MSE difference is marginal (t approximately 2.6, df=8), and the P12 absolute difference is small (0.0048). Please report paired significance tests or bootstrap confidence intervals over the five seeds for the main comparison and for each ablation, and state whether any multiple-comparison correction is applied.
  3. [Section 4.3] The ablation variants are not specified precisely enough to interpret. The paper does not state what replaces the adaptive gate in 'w/o Adaptive Gate' (for example, uniform averaging, fixed interpolation, or no fusion), nor how the support-density cue is removed in 'w/o Support-Density Cue' (for example, dropping the input dimension or replacing rho with a constant). These choices affect both behavior and parameter count, so Table 3 cannot be used to assign credit to individual components as written. Please define each variant explicitly, including the exact input dimensions and parameter counts.
minor comments (5)
  1. [Abstract, Section 1] The text contains typos such as 'proposeMissHyper' and 'outperforms' with missing spaces; please proofread carefully.
  2. [Section 3.2] The temporal window W_w(ell) is used before its clipping rule is specified; please state whether it is centered or causal and how boundaries are handled, since rho is central to the method.
  3. [Table 2] The formatting of bold entries is inconsistent: the HyperIMTS row is not bolded while MissHyper is bolded, and the table caption does not explain the bolding convention; please make this uniform.
  4. [Section 4.4] The window-sensitivity discussion reports 'nearby choices' but does not state the range of neighbors explored or whether those additional test points are from the same held-out test split; please clarify this in the text and the Figure 3b caption.
  5. [Section 5] The limitations paragraph is useful, but it would be even more informative if it also noted that the forecasting outputs are point estimates without uncertainty quantification, which matters for clinical deployment.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: held-out empirical comparison with validation-only hyperparameter selection.

full rationale

MissHyper's central claim is empirical: adding a snapshot-restoration encoder before an unchanged hypergraph backbone reduces MSE/MAE on external benchmarks. The only tuned hyperparameter, the support-density window w, is selected on the validation split and fixed before test evaluation (Section 4.4: 'These values are fixed before test evaluation and used for the main results in Table 2'). The controlled comparison keeps the same backbone and propagation operators, and no parameter is fitted to the test targets. I found no circular step of any enumerated kind: there is no self-definitional relation, no fitted input renamed as prediction, no load-bearing self-citation (baselines are independent works, and the authors do not cite their own prior results as support), no imported uniqueness theorem, and no ansatz smuggled via citation. The skeptic's capacity-confounding point—'w/o Snapshot Restoration' still outperforms HyperIMTS—is a valid ablation-interpretation concern about attribution of the gain, but it does not make the derivation circular; the comparison remains an external, held-out evaluation. Accordingly the circularity score is 0.

Assumptions & free parameters 1 free parameters · 2 assumptions · 0 invented entities

The method introduces no new physical entities, mediators, or latent dimensions beyond ordinary learned embeddings. It reorganizes existing event representations; the only hand-fitted quantity is the support window.

free parameters (1)
  • support-density window size w = P12: 3, MIMIC-III: 7, MIMIC-IV: 6
    Chosen on the validation split (Section 4.4) and fixed before test evaluation. The value controls the temporal scale of the local support cue and is not derived from first principles.
assumptions (2)
  • domain assumption Measurements sharing a timestamp form a coherent patient-state snapshot that is useful for forecasting.
    Central modeling premise in Section 3.3; if false, timestamp aggregation could average unrelated events.
  • domain assumption Local support density is a reliable cue for how much a node should borrow from timestamp context.
    Used throughout Sections 3.2 and 3.4; no theoretical proof, only downstream ablation evidence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecasting." pith.science (2026). https://pith.science/paper/IFCYXGIL

@misc{pith2026260721922,
  author       = {Pith},
  title        = {Pith review of: MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecasting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IFCYXGIL}},
  note         = {Machine review of arXiv:2607.21922}
}
read the original abstract

Clinical irregular multivariate time series are shaped not only by physiological dynamics but also by the measurement process that determines when and what to observe. In event-centric models, however, co-timestamp structure can be flattened too early: measurements acquired at the same timestamp are embedded as isolated nodes, leaving local patient-state context unavailable until later message-passing layers. We study this pre-propagation representation bottleneck and address it by restoring co-timestamp context before message passing begins. We propose MissHyper, a missingness-guided hypergraph forecasting model with pre-propagation synchronicity restoration. MissHyper augments each event with a local support-density cue, aggregates co-timestamp records to recover patient-state context, and uses a missingness-guided gate to adaptively fuse node-specific evidence with the recovered context. Across PhysioNet 2012, MIMIC-III, and MIMIC-IV, MissHyper achieves consistent gains in multi-step forecasting and outperforms a strong hypergraph baseline. These results suggest that improving event initialization can benefit sparse clinical forecasting without requiring a redesigned downstream propagation architecture. Ablations indicate that snapshot restoration, adaptive fusion, and support-density encoding all contribute, pointing to event initialization as a critical design axis for sparse clinical forecasting.

Figures

Figures reproduced from arXiv: 2607.21922 by the authors.

Figure 1
Figure 1. Motivation and core idea of MissHyper. (a) Clinical observations are sparse, asynchronous, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overall framework of MissHyper: restoring co-timestamp clinical snapshots before [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Additional analyses for MissHyper. (a) Component-wise ablation verifies the contribution [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

69 extracted references · 35 canonical work pages

  1. [1]

    PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.Circulation, 101(23):e215–e220, 2000

    Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.Circulation, 101(23):e215–e220, 2000. doi: 10.1161/01.CIR.101.23.e215

    doi:10.1161/01.cir.101.23.e215

  2. [2]

    Scott, Leo A

    Ikaro Silva, George Moody, Daniel J. Scott, Leo A. Celi, and Roger G. Mark. Predicting in-hospital mortality of ICU patients: The PhysioNet/Computing in Cardiology challenge 2012. In2012 Computing in Cardiology, pages 245–248, 2012

    work page 2012

  3. [3]

    Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Moham- mad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 9 MIMIC-III, a freely accessible critical care database.Scientific Data, 3:160035, 2016. doi: 10.1038/sdata.2016.35

    doi:10.1038/sdata.2016.35 2016

  4. [4]

    Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J. Pollard, Sicheng Hao, Benjamin Moody, Brian Gow, Li-wei H. Lehman, Leo A. Celi, and Roger G. Mark. MIMIC-IV, a freely accessible electronic health record dataset. Scientific Data, 10(1):1, 2023. doi: 10.1038/s41597-022-01899-x

    doi:10.1038/s41597-022-01899-x 2023

  5. [5]

    Benchmarking deep learning models on large healthcare datasets.Journal of Biomedical Informatics, 83:112–134,

    Sanjay Purushotham, Chuizheng Meng, Zhengping Che, and Yan Liu. Benchmarking deep learning models on large healthcare datasets.Journal of Biomedical Informatics, 83:112–134,

    work page

  6. [6]

    Kale, Greg Ver Steeg, and Aram Galstyan

    Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg, and Aram Galstyan. Multitask learning and benchmarking with clinical time series data.Scientific Data, 6:96, 2019. doi: 10.1038/s41597-019-0103-9

    doi:10.1038/s41597-019-0103-9 2019

  7. [7]

    Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, and Marzyeh Ghassemi. MIMIC-Extract: A data extraction, preprocessing, and representation pipeline for MIMIC-III. InProceedings of the ACM Conference on Health, Inference, and Learning, pages 222–235, 2020. doi: 10.1145/3368555.3384469

    arXiv 2020

  8. [8]

    Donald B. Rubin. Inference and missing data.Biometrika, 63(3):581–592, 1976. doi: 10.1093/ biomet/63.3.581

    work page 1976

Show all 69 references
  1. [9]

    Schafer and John W

    Joseph L. Schafer and John W. Graham. Missing data: Our view of the state of the art. Psychological Methods, 7(2):147–177, 2002. doi: 10.1037/1082-989X.7.2.147

    2002 doi

  2. [10]

    Roderick J. A. Little and Donald B. Rubin.Statistical Analysis with Missing Data. Wiley, 2 edition, 2002. doi: 10.1002/9781119013563

    2002 doi

  3. [11]

    Sindhu Tipirneni and Chandan K. Reddy. Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series.ACM Transactions on Knowledge Discovery from Data, 16(6):105:1–105:17, 2022. doi: 10.1145/3516367

    2022 doi

  4. [12]

    Warpformer: A multi-scale modeling approach for irregular clinical time series

    Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, and Jia Li. Warpformer: A multi-scale modeling approach for irregular clinical time series. InProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 3273–3285, 2023. doi: 10.1145/ 3580305.3599543

    2023

  5. [13]

    DNA-T: Deformable neighbor- hood attention transformer for irregular medical time series.IEEE Journal of Biomedical and Health Informatics, 28(7):4224–4237, 2024

    Jianxuan Huang, Baoyao Yang, Kejing Yin, and Jingwen Xu. DNA-T: Deformable neighbor- hood attention transformer for irregular medical time series.IEEE Journal of Biomedical and Health Informatics, 28(7):4224–4237, 2024. doi: 10.1109/JBHI.2024.3395446

    2024

  6. [14]

    TimeCHEAT: A channel harmony strategy for irregularly sampled multivariate time series analysis

    Jiexi Liu, Meng Cao, and Songcan Chen. TimeCHEAT: A channel harmony strategy for irregularly sampled multivariate time series analysis. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 18861–18869, 2025. doi: 10.1609/aaai.v39i18.34076

    2025 doi

  7. [15]

    EHRSHOT: An EHR benchmark for few-shot evaluation of foundation models

    Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason Fries, and Nigam Shah. EHRSHOT: An EHR benchmark for few-shot evaluation of foundation models. InAdvances in Neural Information Processing Systems, volume 36, 2023

    2023

  8. [16]

    MOTOR: A time-to-event foundation model for structured medical records

    Ethan Steinberg, Jason Fries, Yizhe Xu, and Nigam Shah. MOTOR: A time-to-event foundation model for structured medical records. InInternational Conference on Learning Representations, 2024

    2024

  9. [17]

    Set functions for time series

    Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt. Set functions for time series. InProceedings of the 37th International Conference on Machine Learning, volume 119 ofProceedings of Machine Learning Research, pages 4353–4363, 2020

    2020

  10. [18]

    Gupta, and Jingbo Shang

    Ranak Roy Chowdhury, Jiacheng Li, Xiyuan Zhang, Dezhi Hong, Rajesh K. Gupta, and Jingbo Shang. PrimeNet: Pre-training for irregular multivariate time series. InProceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 7184–7192, 2023. doi: 10.1609/aaai.v3...

    2023 doi

  11. [19]

    GraFITi: Graphs for fore- casting irregularly sampled time series

    Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Scholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, and Lars Schmidt-Thieme. GraFITi: Graphs for fore- casting irregularly sampled time series. InProceedings of the AAAI Conference on Artificial Intellige...

    2024 doi

  12. [20]

    Irregular multivariate time series forecasting: A transformable patching graph neural networks approach

    Weijia Zhang, Chenlong Yin, Hao Liu, Xiaofang Zhou, and Hui Xiong. Irregular multivariate time series forecasting: A transformable patching graph neural networks approach. InProceed- ings of the 41st International Conference on Machine Learning, volume 235 ofProceedings of Mac...

    2024

  13. [21]

    IMTS is worth time× channel patches: Visual masked autoencoders for irregular multivariate time series prediction

    Zhangyi Hu, Jiemin Wu, Hua Xu, Mingqian Liao, Ninghui Feng, Bo Gao, Songning Lai, and Yutao Yue. IMTS is worth time× channel patches: Visual masked autoencoders for irregular multivariate time series prediction. InProceedings of the 42nd International Conference on Machine Lea...

    2025

  14. [22]

    Hi-Patch: Hierarchical patch GNN for irregular multivariate time series

    Yicheng Luo, Bowen Zhang, Zhen Liu, and Qianli Ma. Hi-Patch: Hierarchical patch GNN for irregular multivariate time series. InProceedings of the 42nd International Conference on Machine Learning, volume 267 ofProceedings of Machine Learning Research, 2025

    2025

  15. [23]

    HyperIMTS: Hypergraph neural network for irregular multivariate time series forecasting

    Boyuan Li, Yicheng Luo, Zhen Liu, Junhao Zheng, Jianming Lv, and Qianli Ma. HyperIMTS: Hypergraph neural network for irregular multivariate time series forecasting. InProceedings of the 42nd International Conference on Machine Learning, volume 267 ofProceedings of Machine Lear...

    2025

  16. [24]

    Steven Cheng-Xian Li and Benjamin M. Marlin. Classification of sparse and irregularly sampled time series with mixtures of expected gaussian kernels and random features. InProceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, pages 484–493, 2015

    2015

  17. [25]

    Learning to detect sepsis with a multitask Gaussian process RNN classifier

    Joseph Futoma, Sanjay Hariharan, and Katherine Heller. Learning to detect sepsis with a multitask Gaussian process RNN classifier. InProceedings of the 34th International Conference on Machine Learning, volume 70 ofProceedings of Machine Learning Research, pages 1174–

  18. [26]

    Yulia Rubanova, Ricky T. Q. Chen, and David K. Duvenaud. Latent ordinary differential equations for irregularly-sampled time series. InAdvances in Neural Information Processing Systems, volume 32, 2019

    2019

  19. [27]

    GRU-ODE-Bayes: Con- tinuous modeling of sporadically-observed time series

    Edward De Brouwer, Jaak Simm, Adam Arany, and Yves Moreau. GRU-ODE-Bayes: Con- tinuous modeling of sporadically-observed time series. InAdvances in Neural Information Processing Systems, volume 32, 2019

    2019

  20. [28]

    ContiF- ormer: Continuous-time transformer for irregular time series modeling

    Yuqi Chen, Kan Ren, Yansen Wang, Yuchen Fang, Weiwei Sun, and Dongsheng Li. ContiF- ormer: Continuous-time transformer for irregular time series modeling. InAdvances in Neural Information Processing Systems, volume 36, 2023

    2023

  21. [29]

    Recurrent neural networks for multivariate time series with missing values.Scientific Reports, 8:6085,

    Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. Recurrent neural networks for multivariate time series with missing values.Scientific Reports, 8:6085,

  22. [30]

    Satya Narayan Shukla and Benjamin M. Marlin. Multi-time attention networks for irregularly sampled time series. InInternational Conference on Learning Representations, 2021

    2021

  23. [31]

    doi: 10.1038/s41598-018-24271-9

    doi

  24. [32]

    Temporal graph ODEs for irregularly-sampled time series

    Alessio Gravina, Daniele Zambon, Davide Bacciu, and Cesare Alippi. Temporal graph ODEs for irregularly-sampled time series. InProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pages 4025–4034, 2024. doi: 10.24963/ijcai.2024/445

    2024 doi

  25. [33]

    Graph-guided network for irregularly sampled multivariate time series

    Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik. Graph-guided network for irregularly sampled multivariate time series. InInternational Conference on Learning Representations, 2022

    2022

  26. [34]

    Satya Narayan Shukla and Benjamin M. Marlin. Interpolation-prediction networks for irregu- larly sampled time series. InInternational Conference on Learning Representations, 2019

    2019

  27. [35]

    Satya Narayan Shukla and Benjamin M. Marlin. A survey on principles, models and methods for learning from irregularly sampled time series.arXiv preprint arXiv:2012.00168, 2021. doi: 10.48550/arXiv.2012.00168. 11

    2012 doi arXiv

  28. [36]

    Steven Cheng-Xian Li and Benjamin M. Marlin. A scalable end-to-end gaussian process adapter for irregularly sampled time series classification. InAdvances in Neural Information Processing Systems, volume 29, pages 1804–1812, 2016

    2016

  29. [37]

    Physiome-ODE: A benchmark for irregularly sampled multivariate time-series forecasting based on biological ODEs

    Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, and Lars Schmidt-Thieme. Physiome-ODE: A benchmark for irregularly sampled multivariate time-series forecasting based on biological ODEs. InInternational Conference on Learning...

    2025

  30. [38]

    Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K

    Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, and Jiayu Zhou. Patient subtyping via time-aware LSTM networks. InProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 65–74, 2017. doi: 10.1145/ 3097983.3097997

    2017

  31. [39]

    Phased LSTM: Accelerating recurrent network training for long or event-based sequences

    Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu. Phased LSTM: Accelerating recurrent network training for long or event-based sequences. InAdvances in Neural Information Processing Systems, volume 29, 2016

    2016

  32. [40]

    Neural controlled differential equations for irregular time series

    Patrick Kidger, James Morrill, James Foster, and Terry Lyons. Neural controlled differential equations for irregular time series. InAdvances in Neural Information Processing Systems, volume 33, 2020

    2020

  33. [41]

    Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K. Duvenaud. Neural ordinary differential equations. InAdvances in Neural Information Processing Systems, volume 31, 2018

    2018

  34. [42]

    Modeling irregular time series with continuous recurrent units

    Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, and Maja Rudolph. Modeling irregular time series with continuous recurrent units. InProceedings of the 39th International Conference on Machine Learning, volume 162 ofProceedings of Machine Learning Research, pages 19388–19405, 2022

    2022

  35. [43]

    Neural flows: Efficient alternative to neural ODEs

    Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, and Stephan Günnemann. Neural flows: Efficient alternative to neural ODEs. InAdvances in Neural Information Processing Systems, volume 34, pages 21325–21337, 2021

    2021

  36. [44]

    BRITS: Bidirectional recurrent imputation for time series

    Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li. BRITS: Bidirectional recurrent imputation for time series. InAdvances in Neural Information Processing Systems, volume 31, 2018

    2018

  37. [45]

    Zame, and Mihaela van der Schaar

    Jinsung Yoon, William R. Zame, and Mihaela van der Schaar. Estimating missing data in temporal data streams using multi-directional recurrent neural networks.arXiv preprint arXiv:1711.08742, 2017. doi: 10.48550/arXiv.1711.08742

    2017 doi arXiv

  38. [46]

    Filling the gaps: Multivariate time series imputation by graph neural networks

    Andrea Cini, Ivan Marisca, and Cesare Alippi. Filling the gaps: Multivariate time series imputation by graph neural networks. InInternational Conference on Learning Representations, 2022

    2022

  39. [47]

    E 2GAN: End-to-end generative adversarial network for multivariate time series imputation

    Yonghong Luo, Ying Zhang, Xiangrui Cai, and Xiaojie Yuan. E 2GAN: End-to-end generative adversarial network for multivariate time series imputation. InProceedings of the Twenty- Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pages 3094–3100. Intern...

    2019

  40. [48]

    SAITS: Self-attention-based imputation for time series

    Wenjie Du, David Côté, and Yan Liu. SAITS: Self-attention-based imputation for time series. Expert Systems with Applications, 219:119619, 2023. doi: 10.1016/j.eswa.2023.119619. 12

    2023

  41. [49]

    CSDI: Conditional score-based diffusion models for probabilistic time series imputation

    Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. CSDI: Conditional score-based diffusion models for probabilistic time series imputation. InAdvances in Neural Information Processing Systems, volume 34, pages 24804–24816, 2021

    2021

  42. [50]

    Convolutional neural networks on graphs with fast localized spectral filtering

    Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. InAdvances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016

    2016

  43. [51]

    The graph neural network model.IEEE Transactions on Neural Networks, 20(1):61–80, 2009

    Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model.IEEE Transactions on Neural Networks, 20(1):61–80, 2009. doi: 10.1109/TNN.2008.2005605

    2009

  44. [52]

    Graph attention networks

    Petar Veliˇckovi´c, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. InInternational Conference on Learning Representations, 2018

    2018

  45. [53]

    Kipf and Max Welling

    Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. InInternational Conference on Learning Representations, 2017

    2017

  46. [54]

    Schoenholz, Patrick F

    Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. Neural message passing for quantum chemistry. InProceedings of the 34th International Conference on Machine Learning, volume 70 ofProceedings of Machine Learning Research, pages 1263–1272, 2017

    2017

  47. [55]

    Hamilton, Rex Ying, and Jure Leskovec

    William L. Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. InAdvances in Neural Information Processing Systems, volume 30, 2017

    2017

  48. [56]

    FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective

    Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pei Wang, Ning An, Longbing Cao, and Zhendong Niu. FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective. InAdvances in Neural Information Processing Systems, volume 36, 2023

    2023

  49. [57]

    Connecting the dots: Multivariate time series forecasting with graph neural networks

    Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xian Wu Chang, and Chengqi Zhang. Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page...

    2020

  50. [58]

    Graph neural flows for unveiling sys- temic interactions among irregularly sampled time series

    Giangiacomo Mercatali, Andre Freitas, and Jie Chen. Graph neural flows for unveiling sys- temic interactions among irregularly sampled time series. InAdvances in Neural Information Processing Systems, volume 37, pages 57183–57206. Curran Associates, Inc., 2024. doi: 10.52202/0...

    2024 doi

  51. [59]

    BigST: Linear complexity spatio-temporal graph neural network for traffic forecasting on large-scale road networks.Proceedings of the VLDB Endowment, 17(5):1081–1090, 2024

    Jindong Han, Weijia Zhang, Hao Liu, Tao Tao, Ning Tan, and Hui Xiong. BigST: Linear complexity spatio-temporal graph neural network for traffic forecasting on large-scale road networks.Proceedings of the VLDB Endowment, 17(5):1081–1090, 2024. doi: 10.14778/ 3641204.3641217

    2024

  52. [60]

    Hypergraph neural networks

    Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. Hypergraph neural networks. InProceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 3558–3565, 2019. doi: 10.1609/aaai.v33i01.33013558

    2019 doi

  53. [61]

    Knowledge- empowered dynamic graph network for irregularly sampled medical time series

    Yicheng Luo, Zhen Liu, Linghao Wang, Junhao Zheng, Binquan Wu, and Qianli Ma. Knowledge- empowered dynamic graph network for irregularly sampled medical time series. InAdvances in Neural Information Processing Systems, volume 37, pages 67172–67199. Curran Associates, Inc., 202...

    2024 doi

  54. [62]

    HyperGCN: A new method for training graph convolutional networks on hypergraphs

    Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. HyperGCN: A new method for training graph convolutional networks on hypergraphs. InAdvances in Neural Information Processing Systems, volume 32, 2019

    2019

  55. [63]

    Dynamic hypergraph neural networks

    Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, and Yue Gao. Dynamic hypergraph neural networks. InProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 2635–2641, 2019. doi: 10.24963/ijcai.2019/366

    2019 doi

  56. [64]

    Song Bai, Feihu Zhang, and Philip H. S. Torr. Hypergraph convolution and hypergraph attention. Pattern Recognition, 110:107637, 2021. doi: 10.1016/j.patcog.2020.107637. 13

    2021

  57. [65]

    Hyper-SAGNN: A self-attention based graph neural network for hypergraphs

    Ruochi Zhang, Yuesong Zou, and Jian Ma. Hyper-SAGNN: A self-attention based graph neural network for hypergraphs. InInternational Conference on Learning Representations, 2020

    2020

  58. [66]

    HGNN+: General hypergraph neural networks

    Yue Gao, Yifan Feng, Shuyi Ji, and Rongrong Ji. HGNN+: General hypergraph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3181–3199, 2023. doi: 10.1109/TPAMI.2022.3182052

    2023

  59. [67]

    Hy- pergraph structure learning for hypergraph neural networks

    Derun Cai, Moxian Song, Chenxi Sun, Baofeng Zhang, Shenda Hong, and Hongyan Li. Hy- pergraph structure learning for hypergraph neural networks. InProceedings of the Thirty- First International Joint Conference on Artificial Intelligence, pages 1923–1929, 2022. doi: 10.24963/ij...

    1923 doi

  60. [69]

    Ada-MSHyper: Adaptive multi- scale hypergraph transformer for time series forecasting

    Zongjiang Shang, Ling Chen, Binqing Wu, and Dongliang Cui. Ada-MSHyper: Adaptive multi- scale hypergraph transformer for time series forecasting. InAdvances in Neural Information Processing Systems, volume 37, 2024. 14

    2024

  61. [2018]

    doi: 10.1016/j.jbi.2018.04.007

    2018 doi

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.