Imagen de Portada

Unscrambling disease progression at scale: fast inference of event permutations with optimal transport

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wijeratne, Peter A., Alexander, Daniel C.
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

LLM enhanced graph inference for long-term disease progression modelling
por: He, Tiantian, et al.
Publicado: (2025)

Learning to Unscramble Feynman Loop Integrals with SAILIR
por: Shih, David
Publicado: (2026)

Convergence of coordinate ascent variational inference for log-concave measures via optimal transport
por: Arnese, Manuel, et al.
Publicado: (2024)

Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories
por: Shih, David
Publicado: (2026)

Causal inference for the expected number of recurrent events in the presence of a terminal event
por: Baer, Benjamin R., et al.
Publicado: (2023)

Investigating the Role of Bilateral Symmetry for Inpainting Brain MRI
por: Kuznetsov, Sergey, et al.
Publicado: (2025)

Mitigating multiple single-event upsets during deep neural network inference using fault-aware training
por: Vinck, Toon, et al.
Publicado: (2025)

Variational inference via radial transport
por: Ghafourpour, Luca, et al.
Publicado: (2026)

Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
por: Young, Elliot H., et al.
Publicado: (2025)

Hardware optimization on Android for inference of AI models
por: Gherasim, Iulius, et al.
Publicado: (2025)

Independent projections of diffusions: Gradient flows for variational inference and optimal mean field approximations
por: Lacker, Daniel
Publicado: (2023)

Capturing Longitudinal Changes in Brain Morphology Using Temporally Parameterized Neural Displacement Fields
por: Shuaibu, Aisha L., et al.
Publicado: (2025)

Stein transport for Bayesian inference
por: Nüsken, Nikolas
Publicado: (2024)

Improving and generalizing flow-based generative models with minibatch optimal transport
por: Tong, Alexander, et al.
Publicado: (2023)

Statistical optimal transport
por: Chewi, Sinho, et al.
Publicado: (2024)

Unrolled denoising networks provably learn optimal Bayesian inference
por: Karan, Aayush, et al.
Publicado: (2024)

Learning Mechanistic Subtypes of Neurodegeneration with a Physics-Informed Variational Autoencoder Mixture Model
por: Pinnawala, Sanduni, et al.
Publicado: (2025)

Simultaneous linear connectivity of neural networks modulo permutation
por: Sharma, Ekansh, et al.
Publicado: (2024)

Predictive variational inference: Learn the predictively optimal posterior distribution
por: Lai, Jinlin, et al.
Publicado: (2024)

Adaptive multi-fidelity optimization with fast learning rates
por: Fiegel, Come, et al.
Publicado: (2026)

Trustworthy predictive distributions for rare events via diagnostic transport maps
por: Cucuzzella, Elizabeth, et al.
Publicado: (2026)

Characterization and forecasting of national-scale solar power ramp events
por: Lanzilao, Luca, et al.
Publicado: (2026)

FiMMIA: scaling semantic perturbation-based membership inference across modalities
por: Emelyanov, Anton, et al.
Publicado: (2025)

High-dimensional semi-supervised learning: in search for optimal inference of the mean
por: Zhang, Yuqian, et al.
Publicado: (2019)

Proximal optimal transport divergences
por: Baptista, Ricardo, et al.
Publicado: (2025)

Learning the irreversible progression trajectory of Alzheimer's disease
por: Wang, Yipei, et al.
Publicado: (2024)

From pixels to planning: scale-free active inference
por: Friston, Karl, et al.
Publicado: (2024)

On amortizing convex conjugates for optimal transport
por: Amos, Brandon
Publicado: (2022)

Online selective conformal inference: adaptive scores, convergence rate and optimality
por: Humbert, Pierre, et al.
Publicado: (2025)

On permutation-invariant neural networks
por: Kimura, Masanari, et al.
Publicado: (2024)

Learning signals defined on graphs with optimal transport and Gaussian process regression
por: Perez, Raphaël Carpintero, et al.
Publicado: (2024)

A unified framework for hard and soft clustering with regularized optimal transport
por: Diebold, Jean-Frédéric, et al.
Publicado: (2017)

Embed and Emulate: Contrastive representations for simulation-based inference
por: Jiang, Ruoxi, et al.
Publicado: (2024)

Bayesian meta-learning for modeling Alzheimer's disease progression
por: Hoffmann, Clara, et al.
Publicado: (2026)

Computing high-dimensional optimal transport by flow neural networks
por: Xu, Chen, et al.
Publicado: (2023)

Convergence of empirical subgradients for optimal transport-based objectives
por: Le, Tam
Publicado: (2026)

Inference via robust optimal transportation: theory and methods
por: Ma, Yiming, et al.
Publicado: (2023)

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
por: Mayfrank, Daniel, et al.
Publicado: (2024)

Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemia
por: Gachon, Erell, et al.
Publicado: (2024)

Snacks: a fast large-scale kernel SVM solver
por: Tanji, Sofiane, et al.
Publicado: (2023)