Intro
Preface
Organization
Contents
Part II
Generative Models
Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks
1 Introduction
2 Related Work
3 Background
4 Methodology
5 Experiments
6 Conclusion
References
Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection
1 Introduction
2 Related Work
2.1 Community Detection
2.2 Node Representation Learning
2.3 Joint Community Detection and Node Representation Learning
3 Methodology
3.1 Problem Formulation

3.2 Variational Model
3.3 Design Choices
3.4 Practical Aspects
3.5 Complexity
4 Experiments
4.1 Synthetic Example
4.2 Datasets
4.3 Baselines
4.4 Settings
4.5 Discussion of Results
4.6 Hyperparameter Sensitivity
4.7 Training Time
4.8 Visualization
5 Conclusion
References
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
1 Introduction
2 Related Work
3 Problem Definition
4 Proposed Algorithm
4.1 GAN Modeling
4.2 Architecture
4.3 Training Procedure
5 Datasets
6 Experiments
6.1 Baselines

6.2 Comparative Evaluation
6.3 Side-by-Side Diagnostics
7 Conclusion
References
The Bures Metric for Generative Adversarial Networks
1 Introduction
2 Method
3 Empirical Evaluation of Mode Collapse
3.1 Artificial Data
3.2 Real Images
4 High Quality Generation Using a ResNet Architecture
5 Conclusion
References
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
1 Introduction
2 Background and Related Work
2.1 Energy-Based Models
2.2 Alternatives to the Softmax Classifier
3 Methodology

3.1 Approach 1: Discriminative Training
3.2 Approach 2: Generative Training
3.3 Approach 3: Joint Training
3.4 GMMC for Inference
4 Experiments
4.1 Hybrid Modeling
4.2 Calibration
4.3 Out-Of-Distribution Detection
4.4 Robustness
4.5 Training Stability
4.6 Joint Training
5 Conclusion and Future Work
References
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty
1 Introduction
1.1 Contributions
2 Background
2.1 Variational Autoencoder
2.2 Latent Variance Estimates of NN

2.3 Mismatch Between the Prior and Approximate Posterior
3 Methodology
3.1 Gaussian Process Encoder
3.2 The Implications of a Gaussian Process Encoder
3.3 Out-of-Distribution Detection
4 Experiments
4.1 Log Likelihood
4.2 Uncertainty in the Latent Space
4.3 Benchmarking OOD Detection
4.4 OOD Polution of the Training Data
4.5 Synthesizing Variants of Input Data
4.6 Interpretable Kernels
5 Related Work
6 Conclusion
References
Variational Hyper-encoding Networks
1 Introduction
2 Variational Autoencoder (VAE)
3 Variational Hyper-encoding Networks