Data driven conditional optimal transport

Tabak, Esteban G.; Trigila, Giulio; Zhao, Wenjun

doi:10.1007/s10994-021-06060-0

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…In summary, this is a first step toward learning to predict contextualized transportation plans through quantum. 1 We use "contextual" rather than "conditional" to differentiate from OT on conditional probabilities (Tabak et al, 2021)…”

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confidence: 99%

A doubly stochastic matrices-based approach to optimal qubit routing

Mariella

Zhuk

2023

Quantum Inf Process

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Optimal Transport (OT) has fueled machine learning (ML) applications across many domains. In cases where paired data measurements (µ, ν) are coupled to a context variable p i , one may aspire to learn a global transportation map that can be parameterized through a potentially unseen context. Existing approaches utilize Neural OT and largely rely on Brenier's theorem. Here, we propose a first-of-its-kind quantum computing formulation for amortized optimization of contextualized transportation plans. We exploit a direct link between doubly stochastic matrices and unitary operators thus finding a natural connection between OT and quantum computation. We verify our method on synthetic and real data, by predicting variations in cell type distributions parameterized through drug dosage as context. Our comparisons to several baselines reveal that our method can capture dose-induced variations in cell distributions, even to some extent when dosages are extrapolated and sometimes with performance similar to the best classical models. In summary, this is a first step toward learning to predict contextualized transportation plans through quantum. 1 We use "contextual" rather than "conditional" to differentiate from OT on conditional probabilities (Tabak et al., 2021)

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confidence: 99%

A doubly stochastic matrices-based approach to optimal qubit routing

Mariella

Zhuk

2023

Quantum Inf Process

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Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay

Bao

Chen

Wang

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay

Bao¹,

Chen²,

Wang³

et al. 2021

Preprint

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Accurate trajectory prediction of vehicles is essential for reliable autonomous driving. To maintain consistent performance as a vehicle driving around different cities, it is crucial to adapt to changing traffic circumstances and achieve lifelong trajectory prediction model. To realize it, catastrophic forgetting is a main problem to be addressed. In this paper, a divergence measurement method based on conditional Kullback-Leibler divergence is proposed first to evaluate spatiotemporal dependency difference among varied driving circumstances. Then based on generative replay, a novel lifelong vehicle trajectory prediction framework is developed. The framework consists of a conditional generation model and a vehicle trajectory prediction model. The conditional generation model is a generative adversarial network conditioned on position configuration of vehicles. After learning and merging trajectory distribution of vehicles across different cities, the generation model replays trajectories with prior samplings as inputs, which alleviates catastrophic forgetting. The vehicle trajectory prediction model is trained by the replayed trajectories and achieves consistent prediction performance on visited cities. A lifelong experiment setup is established on four open datasets including five tasks. Spatiotemporal dependency divergence is calculated for different tasks. Even though these divergence, the proposed framework exhibits lifelong learning ability and achieves consistent performance on all tasks.

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Data driven conditional optimal transport

Cited by 3 publications

References 9 publications

A doubly stochastic matrices-based approach to optimal qubit routing

A doubly stochastic matrices-based approach to optimal qubit routing

Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay

Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay

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