Tarun Reddi PRO
Teen-Different
AI & ML interests
Generative AI, Modular AI Systems, Reinforcement Learning
Recent Activity
posted
an
update
12 days ago
Revisiting LIME (Local Interpretable Model-agnostic Explanations) in the age of foundation models feels archaic, but the intuition holds: accuracy isn't a proxy for understanding.
I ran an experiment pitting CNNs against Transformers using a custom SLIC perturbation pipeline to see what they were actually looking at.
The results say, models are lazy students.
• ViT didn’t see a "Jeep"; it recognized a "muddy road" and used a dataset shortcut to guess the vehicle.
• EfficientNet hallucinated a "toaster" just because it saw a white counter.
High confidence based on background noise is a liability. If you aren't visually auditing your decision boundaries, you're just hoping for the best.
Full breakdown of the "Clever Hans" effect below. 👇
https://teendifferent.substack.com/p/your-features-arent-what-you-think
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a Space
about 1 month ago
HuggingFaceTB/smol-training-playbook
updated
a dataset
about 2 months ago
makodev/KernelBench
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