Robust Physical-World Attacks on Machine Learning Models - Bo Li

Bo Li: Trustworthy Machine Learning: Robustness, Privacy, Generalization, and their InterconnectionsПодробнее

[CVPR'21 WAD] Keynote - Bo Li, UIUCПодробнее

R|P 2018: Bo Li - Machine Learning meets Game TheoryПодробнее

PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous DrivingПодробнее

Joan Bruna - Prospects and Challenges of Machine Learning in the Physical WorldПодробнее

Adversarial Examples for Deep Neural NetworksПодробнее

Adversarial Camouflage: Hiding Physical-World Attacks With Natural StylesПодробнее

Bo Li - Certifiably Robust Learning via Knowledge-Enabled Logical Reasoning | Nuro Technical TalksПодробнее

NVIDIA's Cosmos World Foundation Model Platform for Physical AI (Paper Walkthrough)Подробнее

I can't STOP reading these Machine Learning Books!Подробнее

04/27/2021 -- Bo Li (UIUC)Подробнее

Reinforcement learning in the physical worldПодробнее

Making Machine Learning Robust Against Adversarial InputsПодробнее

Physical Adversarial Examples with Stop SignПодробнее

Bo Li (UIUC) - Trustworthy ML: Robustness, Privacy, Generalization, and their InterconnectionsПодробнее

Security of Deep Learning based Lane Keeping System under Physical-World Adversarial AttackПодробнее

Invited Talk 3: Earlence Fernandes - Physical Attacks on Object DetectorsПодробнее

[DSC 5.0] Security of Machine Learning - Jelena MilosevicПодробнее

ShapeShifter: Adversarial Attack on Deep Learning Object Detector (Faster R-CNN)Подробнее