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Found 17 papers, 5 papers with code
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction
The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods.
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation
We propose a conditional score model to control the solution generation by the input parameters and the fidelity.
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
To generalize Tucker decomposition to such scenarios, we propose Functional Bayesian Tucker Decomposition (FunBaT).
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs).
Multi-Resolution Active Learning of Fourier Neural Operators
To overcome this problem, we propose Multi-Resolution Active learning of FNO (MRA-FNO), which can dynamically select the input functions and resolutions to lower the data cost as much as possible while optimizing the learning efficiency.
Batch Multi-Fidelity Active Learning with Budget Constraints
However, this method only queries at one pair of fidelity and input at a time, and hence has a risk to bring in strongly correlated examples to reduce the learning efficiency.
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
However, the performance of multi-domain PINNs is sensitive to the choice of the interface conditions.
A novel learning-based robust model predictive control energy management strategy for fuel cell electric vehicles
Next, the components including explicit data tables and vehicle velocity estimation are combined with model predictive control (MPC) to release the state-of-the-art energy-saving ability for the multi-freedom system in FCEVs, whose name is LRMPC.
Nonparametric Embeddings of Sparse High-Order Interaction Events
High-order interaction events are common in real-world applications.
Infinite-Fidelity Coregionalization for Physical Simulation
Our model can interpolate and/or extrapolate the predictions to novel fidelities, which can be even higher than the fidelities of training data.
A Self-Guided Framework for Radiology Report Generation
Moreover, SGF successfully improves the accuracy and length of medical report generation by incorporating a similarity comparison mechanism that imitates the process of human self-improvement through compar-ative practice.
Meta-Learning with Adjoint Methods
the initialization, we only need to run the standard ODE solver twice -- one is forward in time that evolves a long trajectory of gradient flow for the sampled task; the other is backward and solves the adjoint ODE.
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Bayesian optimization (BO) is a powerful approach for optimizing black-box, expensive-to-evaluate functions.
Deep Multi-Fidelity Active Learning of High-dimensional Outputs
The training examples can be collected with different fidelities to allow a cost/accuracy trade-off.
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Because click data is often noisy and biased, a variety of methods have been proposed to construct unbiased learning to rank (ULTR) algorithms for the learning of unbiased ranking models.
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
In many applications, the objective function can be evaluated at multiple fidelities to enable a trade-off between the cost and accuracy.
Scalable Variational Gaussian Process Regression Networks
Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable.
