𝐶𝑎𝑟𝑎𝑚𝑖𝑧𝑎𝑟𝑢'𝑠 𝐵𝑙𝑜𝑔 feed:2025:11:17

Introduction to Flow Matching and Diffusion Models

anonymous@undisclosed.example.com (Anonymous) — Mon, 17 Nov 2025 23:44:42 +0000

Introduction to Flow Matching and Diffusion Models

MIT Computer Science Class 6.S184: Generative AI with Stochastic Differential Equations

Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and more! This course aims to build up the mathematical framework underlying these models from first principles. At the end of the class, students will have built a toy image diffusion model from scratch, and along the way, will have gained hands-on experience with the mathematical toolbox of stochastic differential equations that is useful in many other fields. This course is ideal for students who want to develop a principled understanding of the theory and practice of generative AI.

Course Notes

The course notes serve as the backbone of the course and provide a self-contained explanation of all material in the class. In contrast, lecture slides will generally not be self-contained and are intended to provide accompanying visualizations during the lecture. You may view the notes by clicking on the colored link below.

Course Notes

The full video course and slides/course materials can be found here:

Course web page

Robotics, ML, RL, Diffusion-Models, stochastic-differential-equations, SDE, MIT, 2025

Memmo Winter School 2019

anonymous@undisclosed.example.com (Anonymous) — Mon, 17 Nov 2025 23:22:33 +0000

Memmo Winter School 2019

Here is a list of topics that will be covered during this school

Movement primitives
Pinocchio: kinematics and derivatives in C++ and python
Optimal control solvers
Contact planner
Walking robot navigation and state estimation
Motion planning for legged locomotion (tentative title, probably focus on quadrupedal locomotion)
Motion planning workflow for the atalante exoskeleton
TALOS control system architecture and whole body controller
Deep RL
QP-based task-space inverse
Dynamics: theory and implementation
Robust optimization with application to task-space inverse dynamics

Optimization and Learning for Robot Control 2025

anonymous@undisclosed.example.com (Anonymous) — Mon, 17 Nov 2025 22:47:17 +0000

Optimization and Learning for Robot Control 2025