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Showing papers from Imperial College London, Nanyang Technological University Show all papers

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NeurIPS 2026Imperial College LondonImperial College London, NanyangTime series forecasting

End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems

A flow-based estimator enables exact likelihood optimization for recurrent switching dynamical systems, proving broad identifiability and improving disentanglement and forecasting over VAE methods.

Carles Balsells Rodas, Zhengrui Xiang, Francisco Sumba Toral, Yingzhen Li

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 2/5
medium 6/10
strict 1/5
71%Highly rated
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NeurIPS 2026Imperial College LondonImperial College London, NanyangFlow matching

Structured Coupling for Flow Matching

SCFM augments flow matching with structured latent variables and a shared recognition network to jointly learn structured priors and continuous transport maps, enabling unsupervised clustering and disentanglement without sacrificing generative quality.

Francisco Sumba Toral, Carles Balsells Rodas, Yingzhen Li

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 1

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6/20 AI panelreviewers recommend it

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lenient 4/5
medium 2/10
strict 0/5
69%Highly rated
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NeurIPS 2026U CambridgeImperial College LondonImperial College London, NanyangMicrosoft Research New EnglandBayesian & probabilistic methods

Free energy Estimation on Any State Space

The paper generalizes neural-transport-accelerated free energy estimation to arbitrary state spaces, validating it across discrete, multimodal, and autoregressive settings while establishing group-theoretic identities linking time reversal and Doob's transforms.

Jiajun He, Zijing Ou, Francisco Vargas, Yingzhen Li and 3 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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3/20 AI panelreviewers recommend it

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AI panel: 3 of 20 reviewers recommend it
lenient 1/5
medium 2/10
strict 0/5