yashkc2025/low_capacity_nn_behavior
Code for paper "Understanding Generalization, Robustness, and Interpretability in Low-Capacity Neural Networks"
This project helps machine learning engineers and researchers understand how extremely small neural networks can still perform well on image classification tasks. It takes MNIST image data and reveals that even after drastically reducing network weights, these tiny models can classify digits accurately. It also shows that larger, 'overparameterized' networks are more resilient to noisy data, rather than just being more accurate.
No commits in the last 6 months.
Use this if you are exploring the fundamental behavior of neural networks, particularly their efficiency, interpretability, and robustness in resource-constrained environments.
Not ideal if you need a plug-and-play solution for building high-performance, complex AI systems, as this project focuses on foundational research insights rather than immediate application.
Stars
13
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 24, 2025
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