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Found 24 papers, 15 papers with code
Field-based Molecule Generation
This work introduces FMG, a field-based model for drug-like molecule generation.
Latent variable model for high-dimensional point process with structured missingness
Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process.
Estimating treatment effects from single-arm trials via latent-variable modeling
Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.
Learning Space-Time Continuous Neural PDEs from Partially Observed States
We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids.
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that requires using various tricks, such as trajectory splitting, to make model training work in practice.
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data
In this work, we propose the heterogeneous longitudinal VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to heterogeneous data.
Learning Conditional Variational Autoencoders with Missing Covariates
Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates.
Scalable mixed-domain Gaussian processes
Gaussian process (GP) models that combine both categorical and continuous input variables have found use e. g. in longitudinal data analysis and computer experiments.
Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grids
Data-driven neural network models have recently shown great success in modelling and learning complex PDE systems.
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.
Continuous-Time Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time.
Model-based Reinforcement Learning
reinforcement-learning
+1
Multinomial Variational Autoencoders can recover Principal Components
Covariance estimation on high dimensional data is a central challenge across multiple scientific disciplines.
Sample-efficient reinforcement learning using deep Gaussian processes
In model-based reinforcement learning efficiency is improved by learning to simulate the world dynamics.
Longitudinal Variational Autoencoder
Longitudinal datasets measured repeatedly over time from individual subjects, arise in many biomedical, psychological, social, and other studies.
Learning continuous-time PDEs from sparse data with graph neural networks
We demonstrate the model's ability to work with unstructured grids, arbitrary time steps, and noisy observations.
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data
The lgpr tool is implemented as a comprehensive and user-friendly R-package.
Latent Gaussian process with composite likelihoods and numerical quadrature
Clinical patient records are an example of high-dimensional data that is typically collected from disparate sources and comprises of multiple likelihoods with noisy as well as missing values.
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks
We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data.
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Deep learning with differential Gaussian process flows
We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function.
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE).
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates.
Learning unknown ODE models with Gaussian processes
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated.
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and then learns a predictive GP model from the combined data.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent.
