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Found 12 papers, 4 papers with code
Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare.
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered.
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available.
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task.
A Survey on Causal Discovery: Theory and Practice
Understanding the laws that govern a phenomenon is the core of scientific progress.
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains
In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem.
Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches
A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples.
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases
Specifically, this paper addresses the problem of sequentially optimizing for the best diagnosis, leveraging past interactions with the system and their contextual information (i. e. the evidence provided by the users).
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Incomplete data are a common feature in many domains, from clinical trials to industrial applications.
A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks
Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention.
Investigating the Compositional Structure Of Deep Neural Networks
By defining a direct acyclic graph representing the composition of activation patterns through the network layers, it is possible to characterize the instances of the input data with respect to both the predicted label and the specific (linear) transformation used to perform predictions.
CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit
CTBNCToolkit implements the inference algorithm, the parameter learning algorithm, and the structural learning algorithm for continuous time Bayesian network classifiers.
