Hanwei ZHANG

Hanwei ZHANG

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false. Read more

Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

portfolio

publications

NeRFail: Neural Radiance Fields-Based Multiview Adversarial Attack

Published in Proceeding of the 38th AAAI Conference on Artificial Intelligence, 2024

This paper is about adversarial robustness on NeRF. Read more

Recommended citation: Jiang, W., Zhang, H., Wang, X., Guo, Z., & Wang, H. (2024, March). Nerfail: Neural radiance fields-based multiview adversarial attack. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 19, pp. 21197-21205). https://ojs.aaai.org/index.php/AAAI/article/view/30113

talks

A Quick Tour: Deep Learning in Adversarial Context

Published:

I was invited to give a tutorial on adversarial attacks in deep learning for researchers attending the workshop. In this session, I introduced the background, motivation, and fundamental concepts of the problem before discussing class work on adversarial attacks and presenting state-of-the-art advancements, including our work on adversarial attacks on images. I also provided an overview of existing tools for running attacks. Read more

Adversarial Robustness in Deep Learning

Published:

I was invited to give a talk on adversarial attacks in deep learning for bachelor’s students. In this session, I introduced the background, motivation, and fundamental concepts of the problem before discussing class work on adversarial attacks and presenting state-of-the-art advancements, including our work on adversarial attacks on images. Read more

Adversarial Attack in 3D Representation

Published:

I was invited to give a talk on adversarial attacks in 3D representation for master’s students. In this session, I introduced the background, motivation, and fundamental concepts of the problem before presenting our work on adversarial attacks targeting 3D point clouds and Neural Radiance Fields (NeRF). Read more

Unveiling AI: Exploring Neural Networks and the Journey Towards Interpretability

Published:

In this invited talk, we introduced the fundamentals of interpretability in neural networks, aiming to make the topic accessible to university students new to the field. We explored why interpretability is essential, discussed key methods for analyzing neural networks, and highlighted how these insights can pave the way for impactful research. The session aimed to inspire and equip students with the knowledge to embark on their AI research journey. Read more

teaching

Exploring Explainability in Machine Learning

Master Seminar, Saarland University, 2024

This seminar course delves into the crucial and evolving field of explainability in machine learning (ML). As ML models become increasingly complex and integral to various domains, understanding how these models make decisions is essential. This course will explore different methodologies for interpreting ML models, including rule-based, attribution-based, example-based, prototype-based, hidden semantics-based, and counterfactual-based approaches. Through a combination of paper readings, discussions, and presentations, students will gain a comprehensive understanding of the challenges and advancements in making ML models transparent and interpretable. Read more