ai-for-science-papers

Collection of general artificial intelligence papers for applications in science.

1. Uncertainty Estimation

1. DAG-GNN: DAG Structure Learning with Graph Neural Networks

2. Inference in probabilistic graphical models by Graph Neural Networks

2. OT for Domain Adaptation (Data vs. Simulation Discrepancy)

1. An introduction to domain adaptation and transfer learning

2. Optimal Transport for Domain Adaptation

3. Theoretical Analysis of Domain Adaptation with Optimal Transport

4. Learning with Wasserstein barycenters and applications

5. Model Fusion via Optimal Transport

6. Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation

7. Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation

8. Optimal Transport for Multi-source Domain Adaptation under Target Shift

9. DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation

3. Mathematical and Theoretical AI

1. Theoretical Analysis of Domain Adaptation with Optimal Transport

4. Graphical Neural Networks

1. DAG-GNN: DAG Structure Learning with Graph Neural Networks

2. Inference in probabilistic graphical models by Graph Neural Networks

5. Signal Processing

6. Image Segmentation

• Semantic Segmentation

• Instance Segmentation