Hello, ML enthusiasts! 🚀🤖 We analyzed rotational equilibria in our latest work, ROTATIONAL EQUILIBRIUM: HOW WEIGHT DECAY BALANCES LEARNING ACROSS NEURAL NETWORKS

💡 Our Findings: Balanced average rotational updates (effective learning rate) across all network components may play a key role in the effectiveness of AdamW.

🔗 ROTATIONAL EQUILIBRIUM: HOW WEIGHT DECAY BALANCES LEARNING ACROSS NEURAL NETWORKS

Looking forward to hearing your thoughts! Let’s discuss more about this fascinating topic together!

  • @A_AEnglish
    1•8 months ago

    Please explain like I’m a 5 years old.

    Maybe I understand the following :
    (my apologies if this is grossly simplified and doesn’t help)

    1- Better neural network need to contain more (stacked) layers.
    2- input layer at one end of the stack is exposed to messy informations from the real world.
    3- at the other end the output layer provide results from the network.
    4- the first step for making this work is the training of the network during which training, learning is done.
    5- instabilities and stagnation in some layers often occur when learning does not occur in an optimal way. This problem increases exponentially with the number of layers.
    6- here learning is done all at once to all the layers. Something called rotation which I don’t understand, is used to stabilize and optimize the learning.

    I feel this is very different from human learning where it happens in stages : we first learn words, then try to assemble them to form simple sentences, then evolve to make sense of more complex notions and so on. I wish this approach could apply also in artificial intelligence development.