ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
THE RESEARCH ATLAS
GAIA-1: A Generative World Model for Autonomous Driving
Models video, text and vehicle actions as sequences to generate conditioned driving scenes and possible futures.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Distinguish world models from driving policies: predicting how a scene changes is not the same as deciding how to drive safely.
What it connects
Brings sequence modeling and video generation into driving. Reading it beside Cosmos highlights shared questions, not direct inheritance.
World-model research and generation evaluation
Generation quality and controllability do not replace closed-loop driving, real-road testing or long-tail safety evaluation.
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Citation
@misc{hu2023gaia,
title = {GAIA-1: A Generative World Model for Autonomous Driving},
author = {Anthony Hu and Lloyd Russell and Hudson Yeo and Zak Murez and George Fedoseev and Alex Kendall and Jamie Shotton and Gianluca Corrado},
year = {2023},
eprint = {2309.17080},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2309.17080}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
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