Mostafa ElAraby
mostafaelaraby.bsky.social
Mostafa ElAraby
@mostafaelaraby.bsky.social
200 followers 240 following 12 posts
AI researcher @ Mila, UdeM. PhD focused on OOD detection & generalization. Building robust deep learning. Previously: Microsoft ATL, Tensorgraph. #AI #MachineLearning
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Merging LoRA weights? You might be throwing away a powerful security feature! 🤯
A new paper shows unmerged LoRA modules are a game-changer for detecting near-OOD data.
I wrote a blog post breaking down the idea. Check it out!
🔗: www.mostafaelaraby.com/paper%20revi...
PhD students: How much time does your supervisor spend with you?

A Nature survey found ~half spend <1 hour/week with theirs.

Yet, more supervisor time is linked to much higher satisfaction (82% vs 69%). Supportive mentorship is crucial.

Full article: www.nature.com/articles/d41...
#PhDLife
What makes PhD students happy? Good supervision
Supervisors who invest in positive mentoring relationships with their PhD candidates also reap the benefits for their own research.
www.nature.com
Go beyond the data! 🧠 Neural network weights are a rich, untapped resource.
My new blog post dives into Weight Space Learning, exploring how we can analyze models to predict performance and combine them to build better ones.
#ML #DNN #Weight_Space
mostafaelaraby.com/paper%20revi...
GROOD is a practical, post-hoc framework for robust OOD detection & safer AI.

Huge thanks to the team: Mostafa ElAraby, Sabyasachi Sahoo, Yann Pequignot, Paul Novello, Liam Paull.

And collaborators: @Mila_Quebec, @DIRO_UdeM, @UniversiteLaval, & the DEEL project.
The results speak for themselves! 🏆

GROOD achieves SOTA performance on major benchmarks. Highlights:

🔹 84.44% Far-OOD AUROC on CIFAR-100

🔹 94.8% Far-OOD AUROC on ImageNet-1K

🔹 Robust on Transformers (ViT & Swin-T) where others fail!
A picture is worth a thousand words. 📸

While the standard feature space (left) is messy, our gradient-aware space (right) creates a much clearer separation between In-Distribution (blue) and OOD (red/orange) samples!

#DataScience #ComputerVision
What's the secret sauce? ✨

Instead of just feature distance, GROOD uses a synthetic OOD prototype to measure a sample's gradient sensitivity.
ID samples are stable, while OOD samples are sensitive. This creates a powerful, clear signal for detection!
🚀 We're excited to introduce GROOD! A new, training-free method to help models know what they don't know (aka OOD Deteciton).

🔗 Paper: openreview.net/forum?id=2V7...
💻 Code: github.com/mostafaelara...
🎥 Video Overview: www.youtube.com/watch?v=n4-g...