Neural networks adapting to datasets: learning network size and topology
This work addresses the challenge of designing optimal network architectures for specific classification tasks, though it appears incremental as it builds on existing training methods.
The authors tackled the problem of neural networks adapting to datasets by enabling them to learn their size and topology during gradient-based training, resulting in task-specific graph structures that achieve virtually identical performance to standard training.
We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a standard gradient-based training. The resulting network has the structure of a graph tailored to the particular learning task and dataset. The obtained networks can also be trained from scratch and achieve virtually identical performance. We explore the properties of the network architectures for a number of datasets of varying difficulty observing systematic regularities. The obtained graphs can be therefore understood as encoding nontrivial characteristics of the particular classification tasks.