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Found 7 papers, 3 papers with code
Quantitative probing: Validating causal models using quantitative domain knowledge
We present quantitative probing as a model-agnostic framework for validating causal models in the presence of quantitative domain knowledge.
Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures
We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis.
A Graph Neural Network Framework for Causal Inference in Brain Networks
Moreover, dynamic interactions between different brain regions learned by this data-driven approach can provide a multi-modal measure of causal connectivity strength.
Parameterized Reinforcement Learning for Optical System Optimization
Such a combination of both, discrete and continuous parameters is a challenging optimization problem that often requires a computationally expensive search for an optimal system design.
Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps
We demonstrate its performance on a set of several related, but different one-shot imitation tasks, which the agent flexibly solves in an active inference style.
Electricity Load Forecasting -- An Evaluation of Simple 1D-CNN Network Structures
This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future.
A constrained ICA-EMD Model for Group Level fMRI Analysis
The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis.
