LIMINAL fits nested Lindblad models to tomographic data and uses likelihood-ratio tests to identify minimal dynamics for a five-qubit superconducting processor, supporting three-local Hamiltonian terms and two-local dissipation but not three-local dissipation.
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(2022, 2)
14 Pith papers cite this work. Polarity classification is still indexing.
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Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
A neural path estimation approach learns the filtering posterior path measure for stochastic dynamical systems from noisy partial observations by solving a variational stochastic control problem based on the pathwise Zakai equation.
AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.
Derives RAM, a reward-adjusted consistency loss extending diffusion pretraining regression to efficient KL-regularized RL post-training, achieving peak rewards up to 50x faster than Flow-GRPO on Stable Diffusion 3.5M.
NSP model fuses satellite and gauge data with neural processes and SDEs, outperforming 13 baselines and JAXA's operational product on a new 43k-sample US benchmark across six metrics.
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
Global multiqubit Rydberg gates enable break-even measurement-free QEC and lower-shuttling Floquet codes in neutral-atom hardware.
The Weak Penalty Neural ODE uses a weak form loss to filter noise and learn stable chaotic dynamics from noisy observations.
Neptune infers spatiotemporal parameter fields in PDEs from as few as 45 sparse measurements using independent coordinate neural networks, outperforming PINNs and neural operators with lower errors and better extrapolation.
ShockCast is a two-phase ML method that predicts adaptive timestep sizes to model high-speed flows with shocks more efficiently than fixed-step approaches.
A JAX-based framework extending quantum machine learning to pulse-level control with composable ansatzes, end-to-end optimization, and Fourier diagnostics.
Neural networks can detect 38% of summer hypoxic events shelf-wide from satellites with 47% precision, but only within the homogeneous mixed layer.
MPG-NODEs identify power system dynamics more flexibly than standard neural ODEs by using graph message passing, enabling transfer learning for adding or removing lines and units.
citing papers explorer
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Learning Lindblad Dynamics of a Superconducting Quantum Processor
LIMINAL fits nested Lindblad models to tomographic data and uses likelihood-ratio tests to identify minimal dynamics for a five-qubit superconducting processor, supporting three-local Hamiltonian terms and two-local dissipation but not three-local dissipation.
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Is Flow Matching Just Trajectory Replay for Sequential Data?
Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
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Pathwise Learning of Stochastic Dynamical Systems with Partial Observations
A neural path estimation approach learns the filtering posterior path measure for stochastic dynamical systems from noisy partial observations by solving a variational stochastic control problem based on the pathwise Zakai equation.
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AMIGO: a Data-Driven Calibration of the JWST Interferometer
AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.
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Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models
Derives RAM, a reward-adjusted consistency loss extending diffusion pretraining regression to efficient KL-regularized RL post-training, achieving peak rewards up to 50x faster than Flow-GRPO on Stable Diffusion 3.5M.
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Neural Stochastic Processes for Satellite Precipitation Refinement
NSP model fuses satellite and gauge data with neural processes and SDEs, outperforming 13 baselines and JAXA's operational product on a new 43k-sample US benchmark across six metrics.
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Neural CDEs as Correctors for Learned Time Series Models
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
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Multiqubit Rydberg Gates for Quantum Error Correction
Global multiqubit Rydberg gates enable break-even measurement-free QEC and lower-shuttling Floquet codes in neutral-atom hardware.
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A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
The Weak Penalty Neural ODE uses a weak form loss to filter noise and learn stable chaotic dynamics from noisy observations.
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Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
Neptune infers spatiotemporal parameter fields in PDEs from as few as 45 sparse measurements using independent coordinate neural networks, outperforming PINNs and neural operators with lower errors and better extrapolation.
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A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling
ShockCast is a two-phase ML method that predicts adaptive timestep sizes to model high-speed flows with shocks more efficiently than fixed-step approaches.
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Software Between Quantum and Machine Learning -- And Down to Pulses
A JAX-based framework extending quantum machine learning to pulse-level control with composable ansatzes, end-to-end optimization, and Fourier diagnostics.
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The Physical Limit of Neural Hypoxia Detection in the Black Sea from Satellite Observations
Neural networks can detect 38% of summer hypoxic events shelf-wide from satellites with 47% precision, but only within the homogeneous mixed layer.
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Graph Neural Ordinary Differential Equations for Power System Identification
MPG-NODEs identify power system dynamics more flexibly than standard neural ODEs by using graph message passing, enabling transfer learning for adding or removing lines and units.