Opening the Black Box: predicting the trainability of deep neural networks with reconstruction entropy
<|reference_start|>Opening the Black Box: predicting the trainability of deep neural networks with reconstruction entropy: An important challenge in machine learning is to predict the initial conditions under which a given neural network will be trainable. We present a method for predicting the trainable regime in parameter space for deep feedforward neural networks, based on reconstructing the input from subsequent activation layers via a cascade of single-layer auxiliary networks. For both the MNIST and CIFAR10 datasets, we show that a single epoch of training of the shallow cascade networks is sufficient to predict the trainability of the deep feedforward network, thereby providing a significant reduction in overall training time. We achieve this by computing the relative entropy between reconstructed images and the original inputs, and show that this probe of information loss is sensitive to the phase behaviour of the network. Moreover, our approach illustrates the network's decision making process by displaying the changes performed on the input data at each layer. Our results provide a concrete link between the flow of information and the trainability of deep neural networks, further explaining the role of criticality in these systems.<|reference_end|>
@article{thurn2024opening,
    title={Opening the Black Box: predicting the trainability of deep neural
  networks with reconstruction entropy},
    author={Yanick Thurn, Ro Jefferson, Johanna Erdmenger},
    journal={arXiv preprint arXiv:2406.12916},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12916},
    primaryClass={cs.LG cond-mat.dis-nn hep-th stat.ML}
}