Lecture
Socially-aware Artificial Intelligence for Last-mile Mobility
Description
This lecture covers the research on socially-aware artificial intelligence for last-mile mobility, focusing on understanding social etiquettes, anticipating behaviors, and forecasting crowd movements using deep learning models. The instructor presents the challenges in computer vision for transportation, such as limited resolution, safety-critical accuracy, and real-time efficiency. The lecture also delves into the development of a privacy-safe social distancing tool based on visual intelligence, collaborative sampling in generative adversarial networks, and the universal representation learning framework for joint perception, social forecasting, and acting.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (73)
AI for Science
Explores fundamental principles in scientific research, the impact of computers, numerical algorithms, and deep learning in solving high-dimensional problems.
Gradient-Based Algorithms in High-Dimensional Learning
Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.
Veridical Data Science: Responsible, Reliable, Reproducible
By Prof. Bin Yu explores veridical data science, emphasizing responsible, reliable, and reproducible data analysis and decision-making.
Differentiable Ranking and Sorting
Explores differentiable ranking and sorting techniques for machine learning applications.
Safe Machine Learning: Cryptographic Opportunities
Explores the intersection between machine learning and cryptography, focusing on safe machine learning through cryptographic tools and models.