Theoretical Aspects

The empirical success of deep learning presents numerous challenges to theoreticians. In literature, four main factors are addressed:

• Architectures (5 papers)

• Optimization Algorithms (3 papers)

• Generalization/Regularization Techniques (3 papers)

• Stability/Robustness (6 papers)

Understanding the necessity and interplay of these three factors is essential in an analysis of their success. Thus, the theoretical aspects mainly concern:

• Interpretability/ Understanding (1 paper)

• Provability/Stability (3 papers)

• Adversarial Perturbations (19 papers)

• Noisy Labels (2 papers)