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Found 10 papers, 6 papers with code
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision
To harness the interconnected relationships between medical time series and clinical notes, the framework equips a global contrastive loss, aligning a patient's multimodal feature representations with the corresponding discharge summaries.
Federated learning model for predicting major postoperative complications
Federated learning models achieved comparable AUROC performance to central learning models, except for prolonged ICU stay, where the performance of federated learning models was slightly higher than central learning models at UFH GNV center, but slightly lower at UFH JAX center.
Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records
The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning.
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
While we can control the computational cost by choosing the number of layers in standard architectures, in NDEs the number of neural network evaluations for a forward pass can depend on the number of steps of the adaptive ODE solver.
High-performance symbolic-numerics via multiple dispatch
We showcase how this can be used to optimize term construction and give a 113x acceleration on general symbolic transformations.
Stiff Neural Ordinary Differential Equations
We first show the challenges of learning neural ODE in the classical stiff ODE systems of Robertson's problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems.
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
Modern design, control, and optimization often requires simulation of highly nonlinear models, leading to prohibitive computational costs.
Universal Differential Equations for Scientific Machine Learning
In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets."
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
We show high-level functionality for defining neural ordinary differential equations (neural networks embedded into the differential equation) and describe the extra models in the Flux model zoo which includes neural stochastic differential equations.
A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions
In this manuscript we investigate the performance characteristics of Discrete Local Sensitivity Analysis implemented via Automatic Differentiation (DSAAD) against continuous adjoint sensitivity analysis.
Numerical Analysis
