Study materials about "Deep Learning for Molecular Applications".
- Statistical modeling and inference
- Likelihood and Maximum likelihood estimation
- Bayes theorem and Bayesian inference
- Beyond Likelihoodism
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Beyond supervised learning : semi-supervised learning, transfer learning, meta-learning
- Multi-layer perceptron
- Universal approximation theorem
- Inductive biases
- Back propagation
- Vanishing gradient
- Attention
- Gate (Highway network)
- Memory
- The Goal of generative models
- Variational autoencoder (VAE)
- Generative adversarial network (GAN)
- Previous problems on 1st generation generative models and solutions
- Overview on graph neural network
- Relational inference
- Message passing neural network
- Graph convolutional network
- Augmented graph convolutional network
- Graph generative models
- Interpretation of regularization
- L1/L2 regularization
- adversarial training
- ...
- Uncertainty in deep learning
- Variational inference
- MC-dropout network
- Uncertainty quantification
- Uncertainty-aware deep learning
- Detecting out-of-distribution samples
- Adversarial examples
- Domain-adaptation
- Few-shot learning
- Applications on quantum chemistry problems
- Prediction of molecular properties
- De novo molecular design with generative models