Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts | Paper Citation Record

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts write newline

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This paper cites Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Synthetic Control Chart Time Series

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This paper cites G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C

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This paper cites Invariant Risk Minimization.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Invariant Risk Minimization

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This paper cites Robust solutions of optimization problems affected by uncertain probabilities.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Robust solutions of optimization problems affected by uncertain probabilities

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This paper cites and Lelarge, M.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts and Lelarge, M

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This paper cites A Foundation Model for the Earth System.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts A Foundation Model for the Earth System

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This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q

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This paper cites Message Passing Neural PDE Solvers.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Message Passing Neural PDE Solvers

Reference 10

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This paper cites Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data

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This paper cites Multitask learning.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Multitask learning

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This paper cites T., Rubanova, Y., Bettencourt, J., and Duvenaud, D.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts T., Rubanova, Y., Bettencourt, J., and Duvenaud, D

Reference 13

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This paper cites Towards understanding the mixture-of-experts layer in deep learning.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Towards understanding the mixture-of-experts layer in deep learning

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Unresolved cited work

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This paper cites S., Giampaolo, F., Rozza, G., Raissi, M., and Piccialli, F.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts S., Giampaolo, F., Rozza, G., Raissi, M., and Piccialli, F

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This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

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This paper cites ODEF ormer: Symbolic regression of dynamical systems with transformers.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts ODEF ormer: Symbolic regression of dynamical systems with transformers

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This paper cites and Giltinan, D.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts and Giltinan, D

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This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Model-agnostic meta-learning for fast adaptation of deep networks

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts W., Rezende, D., and Eslami, S

Reference 22

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts and Bengio, Y

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This paper cites Out-of-Domain Generalization in Dynamical Systems Reconstruction.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Out-of-Domain Generalization in Dynamical Systems Reconstruction

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 26

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts and Ruthotto, L

Reference 27

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Neural networks: a comprehensive foundation

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Mixture of A Million Experts

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Poseidon: Efficient Foundation Models for PDEs

Reference 30

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Generalized Teacher Forcing for Learning Chaotic Dynamics

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Meta-learning in neural networks: A survey

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts LoRA: Low-Rank Adaptation of Large Language Models

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts A., Jordan, M

Reference 34

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Mixtral of Experts

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts On Neural Differential Equations

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts and Garcia, C

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Neural controlled differential equations for irregular time series

Reference 39

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Generalizing to new physical systems via context-informed dynamics model

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts o wer, M., Lottes, J., Rasp, S., D \

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts K., Benet, J

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Out-of-distribution generalization via risk extrapolation (rex)

Reference 44

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Alternating minimizations converge to second-order optimal solutions

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Mixture-of-transformers: A sparse and scalable architecture for multi-modal foundation models

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Flow Matching for Generative Modeling

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Flow straight and fast: Learning to generate and transfer data with rectified flow

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Least squares quantization in pcm

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts ClimaX: A foundation model for weather and climate

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This paper cites D., Barton, D.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts D., Barton, D

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This paper cites Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-Modulation.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-Modulation

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This paper cites D., Barton, D.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts D., Barton, D

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This paper cites and Chan, A.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts and Chan, A

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.225067Z

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Observation a9d659bd-687e-46ec-84d3-a664065330a6 · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Universal Differential Equations for Scientific Machine Learning

Reference 55

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Searching for Activation Functions

Reference 56

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Observation b83e2571-e19a-48f6-857b-253018a62fbb · outbound

This paper cites Efficient amortised bayesian inference for hierarchical and nonlinear dynamical systems.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Efficient amortised bayesian inference for hierarchical and nonlinear dynamical systems

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.209839Z

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Observation bf4ba866-c248-49ea-a7e0-e0ffa7b663b6 · outbound

This paper cites W., Hashimoto, T.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts W., Hashimoto, T

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.194007Z

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Observation e36b42d7-0e1f-4903-a812-5c6f89890fea · outbound

This paper cites Zebra: In-Context Generative Pretraining for Solving Parametric PDEs.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Zebra: In-Context Generative Pretraining for Solving Parametric PDEs

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T19:48:11.505739Z

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Observation 3ab11535-10b0-40b6-89dc-1ea4f6fa0be3 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.179508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02d4347a-0bc9-4bdd-8891-a45a8fa9c2df · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T19:48:11.514894Z

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Observation 88645601-42f1-41b6-9165-39537d7b2e02 · outbound

This paper cites P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., et al.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., et al

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.163163Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c5485bd-cb29-47bf-b1be-4150ddf751b7 · outbound

This paper cites Differentiable clustering with perturbed spanning forests.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Differentiable clustering with perturbed spanning forests

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.147064Z

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Observation 2330552a-3092-4b47-82a5-f910f7781363 · outbound

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Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T19:48:12.129797Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8c222053-cbec-4c66-9f5c-c9585c0aab41 · outbound

This paper cites W., and Gholami, A.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts W., and Gholami, A

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.114806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b789390-9c3d-403a-9148-e51884ec5625 · outbound

This paper cites Learning neural pde solvers with parameter-guided channel attention.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Learning neural pde solvers with parameter-guided channel attention

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.098126Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 046d5a52-1823-4def-b63a-7ab533b1680b · outbound

This paper cites Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.082884Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ed7c434-4c81-4a68-91a6-d74f94bcb9b8 · outbound

This paper cites Meta-learning dynamics forecasting using task inference.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Meta-learning dynamics forecasting using task inference

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.066207Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc5d8023-ba73-4f6d-976d-9fff9fccb73b · outbound

This paper cites A proposal on machine learning via dynamical systems.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts A proposal on machine learning via dynamical systems

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T19:48:11.552296Z

Source-reported events for the cited work

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Observation 4f3e9c1d-7ae0-4fb7-a348-258ae8945f2c · outbound

This paper cites Leads: Learning dynamical systems that generalize across environments.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Leads: Learning dynamical systems that generalize across environments

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.037965Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e655414b-70f4-4fe0-941d-e8e3f2149426 · outbound

This paper cites Self-supervised contrastive pre-training for time series via time-frequency consistency.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Self-supervised contrastive pre-training for time series via time-frequency consistency

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.022221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22733b02-3708-4ddf-b9d5-78b4560121c8 · outbound

This paper cites C., Dvornek, N., Papademetris, X., and Duncan, J.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts C., Dvornek, N., Papademetris, X., and Duncan, J

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:12.005977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f645055c-e18e-4469-acc8-177df022ca1c · outbound

This paper cites Fast context adaptation via meta-learning.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Fast context adaptation via meta-learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:48:11.990193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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