Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

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.
Designing deep learning models that work well for a task requires an extensive process of iterative architecture engineering and tuning.
These design decisions are largely made by human experts guided by a combination of intuition, grid search, and search heuristics.Meta-learning aims to automate model design by using machine learning to discover good architecture and hyperparameter choices.
Recent advances in meta-learning using Reinforcement Learning (RL) have made promising strides towards accelerating or even eliminating the manual parameter search.
For example, Neural Architecture Search (NAS) has successfully discovered novel network architectures that rival or surpass the best human-designed architectures on challenging benchmark image recognition tasks .
However, naively applying reinforcement learning to each new task for automated model construction requires sampling, constructing, and training hundreds to thousands of networks to relearn how to generate models from scratch.
Human experts, on the other hand, can design and tune networks based on knowledge about underlying dependencies in the search space and experience with prior tasks.
We therefore aim to automatically learn and leverage the same information.In this paper, we present Multitask Neural Model Search (MNMS), an automated model construction framework that finds the best performing models in the search space for multiple tasks simultaneously.
We then show that a MNMS framework that has been pre-trained on previous tasks can construct the best performing model for entirely new tasks in significantly less time.
Summary.
Machine learning model design choices do not exist in a vacuum.
Human experts design good models by leveraging significant prior knowledge about the intuitive relationships between these model parameters, and the performance obtained by different model designs on similar tasks.
Automated model design algorithms, too, can and should learn from the models they have discovered for prior tasks.
This paper demonstrates that Multitask Neural Model Search can discover good, differentiated model designs for multiple tasks simultaneously, while learning task embeddings that encode meaningful relationships between tasks.
We then show that multitask training provides a good baseline for transfer learning to future tasks, allowing the MNMS framework to start from a better location in the search space and converge more quickly to high-performing designs.Limitations and future work.
While the current work demonstrates that the MNMS framework can be used for multitask training and transferable architecture searches, much work remains to determine the scalability of this approach.
The results of this study offer several particularly promising avenues for future research.
First, studying the effects of additional simultaneous tasks on framework performance is an obvious next step in multitask training.
The current framework trains the learned task embeddings by passing them directly into the controller RNN along with the sampled action embeddings.
We anticipate that a more complex pre-processing structure, such as a simple encoder-decoder, could better transform these task embeddings to be used by the controller.
Additionally, we currently leverage the distributed training structure described by Zoph and Le, which trains multiple sampled child architectures in parallel and asynchronously updates a shared controller parameter server .
However, as we continue to scale the MNMS framework for additional simultaneous tasks, future work remains to optimize a parallel training structure and schedule specifically for efficient multitask training.Experimenting with broader richer hyperparameter search spaces also offers an exciting line of future work.
For our current tasks, we defined a search space that encompassed a range of general design choices, including both real-valued parameters (such as learning rates and regularization weights) and higher-level parameters (such as the choice of word embedding table).
However, we are actively adapting the controller to sample continuous real-valued parameters, rather than discrete choices from a set of predefined values, which would give the framework much greater flexibility in specifying models.
Additionally, we plan to continue expanding the range of modular, higher-level parameter choices in the search space.
Allowing the controller to compose these building blocks, rather than more granular design choices, can allow the framework to construct more complex architectures in much less time.Finally, much work remains to explore cases when transfer learning is and is not effective within RL-based architecture search frameworks such as MNMS.
We are particularly interested in studying how transfer learning can be used to design architectures for tasks that were previously considered too resource intensive for standard NAS.
For example, adapted NAS for the ImageNet classification task by directly modifying the architecture designed for a simpler image classification task.
However, pretraining the architecture search framework itself on more computationally feasible tasks, rather than transferring the discovered architectures, would be a significant step towards tackling these difficult search domains.
We present Multitask Neural Model Search, a Meta-learner that can design models for multiple tasks simultaneously and transfer learning to unseen tasks.