Graham discusses the use of Bayesian optimization for hyperparameter tuning and architecture search in deep learning. He highlights a key achievement in developing a kernel for assessing architectural similarity, which allows for efficient exploration of potential fusion architectures. However, the iterative nature of this method can lead to slow evaluations, prompting exploration of alternative approaches like stochastic regularization for architecture search.
