Rethinking Regularization
Regularizers play a crucial role in training neural networks, but their traditional use may hinder the emergence of complex behaviors like adversarial robustness. Techniques such as batch normalization not only simplify training but also impose implicit biases that can prevent reaching optimal solutions. Exploring new regularization strategies could accelerate the development of desired training dynamics, allowing for more effective learning earlier in the process.In this clip
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Machine Learning Street Talk (MLST)
Want to Understand Neural Networks? Think Elastic Origami! - Prof. Randall Balestriero
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