Generative adversarial network

Applied sciences
Information engineering
Machine learning
Artificial neural networks

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Variational Auto-Encoders and NVIB

Explores Variational Auto-Encoders, Bayesian inference, attention-based latent spaces, and the effectiveness of Transformers in language processing.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Deep Generative Models

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Robustness and Diffusion Models

Explores robustness in GAN training, Gaussian algorithms, saddle points, and mixed Nash equilibrium.

Adversarial Training: Optimization and Applications

Explores adversarial training optimization, practical implementation, interpretability, fairness, Wasserstein distance, and Wasserstein GANs.

Adversarial Machine Learning

Explore the vulnerabilities of neural networks to adversarial attacks and the strategies to enhance model robustness.

Diffusion Models and Robustness

Explores diffusion models, GAN training challenges, SDE-based generative models, and robustness in deep learning.

Adversarial Machine Learning: Fundamentals and Techniques

Explores adversarial machine learning, covering the generation of adversarial examples, robustness challenges, and techniques like Fast Gradient Sign Method.

Adversarial Training: Theory and Applications

Covers the practical implementation and applications of adversarial training, Generative Adversarial Networks, distance between distributions, and enforcing 1-Lipschitz in GANs.

Generative Modeling: Then, Now and Tomorrow?

Explores the evolution of generative modeling, from traditional methods to cutting-edge advancements, addressing challenges and envisioning future possibilities.