Generative Memory Lab Courses

The Diffusion Duality

Explore the mathematical foundations and theoretical insights of diffusion models through Subham Sahoo's groundbreaking research on dual perspectives in generative modeling.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

Neural Entropy - Diffusion Models are Kelly Gamblers

Explore cutting-edge research on neural entropy and diffusion models as Kelly gamblers through Akhil Premkumar's comprehensive presentation of two groundbreaking papers.

• YouTube
• 1 hour 59 minutes
• Self-Paced
• Free Video

Entropic Time Schedulers for Generative Diffusion Models

Discover how entropic time schedulers optimize generative diffusion models for improved sampling efficiency and quality in this advanced machine learning presentation.

• YouTube
• 1 hour 28 minutes
• Self-Paced
• Free Video

CANDI - Hybrid Discrete-Continuous Diffusion Models

Discover CANDI, a novel approach combining discrete and continuous diffusion models for advanced generative AI applications in machine learning research.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

CoVAE - Consistency Training of Variational Autoencoders

Discover how consistency training improves Variational Autoencoders through CoVAE methodology, enhancing generative model performance and stability in deep learning applications.

• YouTube
• 1 hour 28 minutes
• Self-Paced
• Free Video

Feedback Guidance of Diffusion Models

Explore how diffusion models can be guided through feedback mechanisms in this research presentation by Félix Koulischer from the Generative Memory Lab.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

Classifier-Free Guidance - From High-Dimensional Analysis to Generalized Guidance Forms

Explore advanced classifier-free guidance techniques through high-dimensional analysis and discover generalized guidance forms for improved generative model control.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

Information-Theoretic Diffusion

Explore how information theory enhances diffusion models through Xianghao Kong's research on theoretical foundations and practical improvements for generative AI.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

Critical Windows: Non-Asymptotic Theory for Feature Emergence in Diffusion Models

Explore non-asymptotic theory for feature emergence in diffusion models, focusing on critical windows and their implications for generative AI and machine learning.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

Explore discrete diffusion modeling techniques for estimating data distribution ratios, enhancing generative AI capabilities and understanding.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Bridging Associative Memory and Probabilistic Modeling

Explore the connection between associative memory and probabilistic modeling in this insightful presentation by Rylan Schaeffer.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Spontaneous Symmetry Breaking in Generative Diffusion Models

Explore symmetry breaking in generative diffusion models, uncovering insights into their behavior and potential applications in AI and machine learning.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Action Matching: Learning Stochastic Dynamics from Samples

Learn stochastic dynamics from samples using action matching techniques. Explore innovative approaches to understanding and modeling complex systems through data-driven methods.

• YouTube
• 1 hour 34 minutes
• Self-Paced
• Free Video

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces

Explore generative diffusion models in discrete-state spaces through the innovative Blackout Diffusion approach, presented by researcher Yen Ting Lin.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

Reflected Diffusion Models

Explore innovative techniques for enhancing diffusion models, improving sample quality and efficiency in generative AI applications.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video