Title: Transfer Learning to Learn with Multitask Neural Model Search,
Abstract: Deep learning models require extensive architecture design exploration and
hyperparameter optimization to perform well on a given task. The exploration of
the model design space is often made by a human expert, and optimized using a
combination of grid search and search heuristics over a large space of possible
choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach
that has been proposed to automate architecture design. NAS has been
successfully applied to generate Neural Networks that rival the best
human-designed architectures. However, NAS requires sampling, constructing, and
training hundreds to thousands of models to achieve well-performing
architectures. This procedure needs to be executed from scratch for each new
task. The application of NAS to a wide set of tasks currently lacks a way to
transfer generalizable knowledge across tasks. In this paper, we present the
Multitask Neural Model Search (MNMS) controller. Our goal is to learn a
generalizable framework that can condition model construction on successful
model searches for previously seen tasks, thus significantly speeding up the
search for new tasks. We demonstrate that MNMS can conduct an automated
architecture search for multiple tasks simultaneously while still learning
well-performing, specialized models for each task. We then show that
pre-trained MNMS controllers can transfer learning to new tasks. By leveraging
knowledge from previous searches, we find that pre-trained MNMS models start
from a better location in the search space and reduce search time on unseen
tasks, while still discovering models that outperform published human-designed
models.