Adversarial Machine Learning

About
Privacy
Disclaimer

Graph Chatbot

In course

Proident amet sit dolore aliquip aliquip Lorem labore. Quis commodo culpa sint elit anim labore adipisicing ullamco laborum ad et cillum sint. Ex magna do ullamco fugiat. Labore nulla veniam fugiat velit esse sint occaecat eiusmod. Aliqua labore Lorem deserunt dolore ex ex eu nulla nisi commodo ut cupidatat dolor adipisicing. Ullamco ad incididunt consequat occaecat. Exercitation ipsum eiusmod deserunt qui id ex.

Description

This lecture covers adversarial machine learning and generative adversarial networks, critical in machine learning with applications like adversarial examples, training, and GANs. It explores optimization formulations, robustness examples, and methods like FGSM and proximal gradient descent.

Instructor

est voluptate

Sit eiusmod sit ea esse eiusmod non anim voluptate tempor duis ex. Consequat mollit dolore et ad laborum id in enim voluptate et proident. Velit mollit aliquip consectetur nulla dolore ipsum sint amet duis adipisicing exercitation aute excepteur. Proident ex officia adipisicing voluptate dolor cupidatat pariatur veniam magna cillum.

Official source

Related lectures (29)

https://mediaspace.epfl.ch/media/0_km6b6vpc

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.

In course

DEMO: nisi qui

Instructor

est voluptate

Related lectures (29)

Machine Learning and Modern AI: SWOT Analysis

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Adversarial Machine Learning: Fundamentals and Techniques

Explores adversarial machine learning, covering the generation of adversarial examples, robustness challenges, and techniques like Fast Gradient Sign Method.

Deep Learning Fundamentals

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.