Probabilistic Neural Architecture Search
This work addresses scalability issues in NAS for researchers and practitioners, enabling more efficient architecture searches on large-scale problems, though it is incremental as it builds on existing NAS methods.
The paper tackles the high memory requirements and computational complexity in neural architecture search (NAS) by proposing PARSEC, a probabilistic approach that reduces memory usage to that of training a single architecture while maintaining state-of-the-art complexity, enabling searches on more complex architectures and larger datasets. It outperforms methods with double its computational cost and matches those with costs three orders of magnitude larger on CIFAR-10 and ImageNet.
In neural architecture search (NAS), the space of neural network architectures is automatically explored to maximize predictive accuracy for a given task. Despite the success of recent approaches, most existing methods cannot be directly applied to large scale problems because of their prohibitive computational complexity or high memory usage. In this work, we propose a Probabilistic approach to neural ARchitecture SEarCh (PARSEC) that drastically reduces memory requirements while maintaining state-of-the-art computational complexity, making it possible to directly search over more complex architectures and larger datasets. Our approach only requires as much memory as is needed to train a single architecture from our search space. This is due to a memory-efficient sampling procedure wherein we learn a probability distribution over high-performing neural network architectures. Importantly, this framework enables us to transfer the distribution of architectures learnt on smaller problems to larger ones, further reducing the computational cost. We showcase the advantages of our approach in applications to CIFAR-10 and ImageNet, where our approach outperforms methods with double its computational cost and matches the performance of methods with costs that are three orders of magnitude larger.