Diffusion Models - PyTorch Implementation
Learn to implement and train diffusion models in PyTorch, covering unconditional and conditional versions, classifier-free guidance, and exponential moving average. Gain hands-on experience with state-of-the-art generative AI techniques.
Stable Diffusion
Explore latent diffusion models for AI image generation, including unconditional generation and super-resolution techniques.
Denoising Diffusion Models for Denoising Diffusion MRI - Tiange Xiang
Explore innovative self-supervised denoising techniques for diffusion MRI using denoising diffusion models. Learn about a three-stage framework integrating statistic-based theory for improved medical imaging quality.
Deep Generative Models - Autoregressive Models - Lecture 3
Explore autoregressive models in deep learning, focusing on their architecture, training, and applications in generating sequential data like text and images.
Deep Generative Models - Diffusion Models for Discrete Data - Lecture 18
Explore diffusion models for discrete data in this Stanford lecture, covering advanced techniques in deep generative modeling for AI applications.
Deep Generative Models - Lecture 6: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this Stanford lecture by Prof. Stefano Ermon.
Deep Generative Models - Energy Based Models - Lecture 11
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
From Large Language Models to Large Multimodal Models - Stanford CS25 - Lecture 4
Explore the evolution from large language models to multimodal AI, focusing on CogVLM and CogAgent. Learn about integrating visual perception with language capabilities and future research directions.
Deep Learning in Life Sciences - Spring 2021
Explore cutting-edge applications of deep learning in genomics, drug design, protein folding, pathology, and neuroscience. Gain insights into AI's transformative potential in life sciences research and healthcare.
Swimming Without a Lifeguard in the Ocean of Ideas - Abstraction and Unsupervised Deep Learning
Explore unsupervised deep learning techniques for discovering hierarchical latent feature spaces, improving interpretability and performance in downstream tasks, and bridging neural and symbolic learning systems.
Generative Models for Image Synthesis
Explore cutting-edge generative models for image synthesis, translation, and defect detection. Learn about GANs, unsupervised techniques, and interactive applications in this advanced AI seminar.
PROTPARDELLE: An All-Atom Protein Generative Model - Broad Institute Machine Learning in Drug Discovery Symposium 2023
Explore PROTPARDELLE, an all-atom protein generative model, presented by Stanford's Possu Huang at the Broad Institute's Machine Learning in Drug Discovery Symposium.
How Well Does Diffusion Model Generate? Exploring Generalization in Emerging Settings
Explore the capabilities and limitations of diffusion models in generative tasks, focusing on emerging generalization settings and their implications.
Generative Models
Explore denoising autoencoders, VAEs, GANs, and RBMs in this comprehensive introduction to generative models, covering key concepts and applications in machine learning.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, VAEs, and GANs. Learn about latent variables, representation learning, and applications in image generation and domain transformation.
Beyond Deep Learning - Learning+Reasoning
Explore advanced AI concepts beyond deep learning, focusing on integrating reasoning with learning for more robust and interpretable systems in this IBM Research lecture.
Introduction to Deep Learning - MIT 2018
Explore foundations of deep learning, including perceptrons, neural networks, loss functions, and optimization techniques. Gain insights into training challenges and strategies for effective model development.
Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum
Explore how machines can achieve more human-like learning through probabilistic programming, intuitive psychology, and simulation engines. Discover insights on core knowledge and one-shot learning in AI.
Your Brain on Energy-Based Models - Applying and Scaling EBMs to Problems
Explore energy-based models in machine learning, covering applications, scaling techniques, and their impact on classification, calibration, and robustness in AI systems.
Hacking and Artificial Intelligence: A 9-Year Research Journey - Red Team Perspective
Exploración de 9 años sobre IA en ciberseguridad: desde detección de malware hasta modelos generativos, abordando aspectos defensivos y ofensivos con demostraciones prácticas y análisis de implicaciones éticas.
Fundamentals and Trends in Vision and Image Processing - Multimodal Generative Networks - Aula 01
Explore multimodal generative networks in vision and image processing with Professor Luiz Velho. Gain insights into cutting-edge techniques and applications in this field.