Machine Learning with Graphs - Fall 2019
Comprehensive exploration of machine learning with graphs, covering network properties, community structures, graph neural networks, and their applications in various domains.
Generative Models With Domain Knowledge for Weakly Supervised Clustering
Explore deep generative models for weakly supervised clustering, incorporating domain knowledge and survival data to guide algorithms towards medically meaningful findings in biomedical datasets.
Deep Generative Models - Discrete Latent Variable Models - Lecture 17
Explore discrete latent variable models in deep generative modeling, covering key concepts and applications in AI and machine learning.
Stanford CS236: Deep Generative Models - Lecture 13 - Score Based Models
Explore score-based models in deep generative modeling, covering theoretical foundations and practical applications in this advanced Stanford lecture on AI and machine learning.
Deep Generative Models - Lecture 5: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this comprehensive Stanford lecture.
Stanford CS236: Deep Generative Models - Score Based Diffusion Models - Lecture 16
Explore score-based diffusion models in deep generative modeling, covering theoretical foundations and practical applications in AI and machine learning.
Deep Generative Models - Maximum Likelihood Learning - Lecture 4
Explore maximum likelihood learning in deep generative models with Stanford's Stefano Ermon. Gain insights into advanced AI techniques and their applications in this comprehensive lecture.
Deep Generative Models - Lecture 2: Background
Explore fundamental concepts and background for deep generative models, covering essential topics to build a strong foundation in this advanced machine learning field.
Deep Generative Models - Energy Based Models - Lecture 12
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights into this powerful approach for modeling complex data distributions.
Deep Generative Models - Normalizing Flows - Lecture 9
Explore normalizing flows in deep generative models, focusing on their principles, applications, and recent advancements in this Stanford lecture by Prof. Stefano Ermon.
Deep Generative Models - Energy Based Models - Lecture 14
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
Evaluation of Generative Models - Stanford CS236 Deep Generative Models Lecture 15
Explore evaluation methods for deep generative models, including metrics and techniques to assess model performance, quality, and effectiveness in various applications.
Stanford CS236: Deep Generative Models - Lecture 7 - Normalizing Flows
Explore normalizing flows in deep generative models with Stanford's Stefano Ermon. Learn advanced techniques for creating flexible and expressive probability distributions in machine learning.
Stanford CS236 - Deep Generative Models - Lecture 1: Introduction
Explore deep generative models with Stanford's Prof. Ermon. Learn key concepts, applications, and cutting-edge techniques in this comprehensive introduction to a rapidly evolving field of AI.
Neural SDEs - Deep Generative Models in the Diffusion Limit - Maxim Raginsky
Explore deep generative models through stochastic control, diffusion limits, and neural nets. Learn about efficient sampling, expressiveness quantification, and unbiased simulation techniques.
Dynamical Systems in Deep Generative Modelling
Explore the intersection of dynamical systems and deep generative modeling, uncovering innovative approaches for creating advanced AI models and understanding complex data patterns.
Toward Brain Computer Interface - Deep Generative Models for Brain Reading
Explore deep generative models for brain reading, covering GAN implementation, image reconstruction from brain signals, and evolving latent codes using genetic algorithms.
Model Validation Using Deep Generation of Stress Data
Discover advanced techniques for model robustness validation through deep generative models, focusing on stress testing, performance assessment, and handling unlikely events in data science applications.
Topographic VAEs Learn Equivariant Capsules - Machine Learning Research Paper Explained
Explore Topographic VAEs: a novel approach to deep generative models with organized latent variables, bridging topographic organization and equivariance in neural networks for improved feature learning and transformation handling.
Deep Generative Models and Stable Diffusion: Revolution in Visual Synthesis - Lecture by Björn Ommer
Explore deep generative models, focusing on diffusion models and Stable Diffusion. Learn about visual synthesis advancements, efficiency improvements, and future developments in this cutting-edge field.
Scientific Discovery and Unsupervised Disentanglement - Yair Weiss, PhD
Explore scientific discovery and unsupervised disentanglement with Prof. Yair Weiss. Learn about ML for scientific discovery, deep generative models, and new possibility theorems in this insightful talk on machine learning advancements.