DeepArchitect: Automatically Designing and Training Deep Architectures (1704.08792v1)

Abstract: In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular language that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree-structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequential model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. In addition, these experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available.

• The paper introduces a modular language that defines complex search spaces for neural architectures and hyperparameters.
• It employs structured search algorithms like MCTS and SMBO, demonstrating superior performance on CIFAR-10 over random search.
• The work reduces manual tuning, streamlining automated model discovery and paving the way for future deep learning innovations.

Overview of DeepArchitect: Automatically Designing and Training Deep Architectures

The paper, "DeepArchitect: Automatically Designing and Training Deep Architectures," explores the automation of neural network architecture design by presenting a structured framework capable of eliminating the intuitive trial-and-error process typically enforced upon human experts. The authors propose a modular and extensible language for defining search spaces over architectures and their hyperparameters. This language allows users to express complex search spaces efficiently and supports automated model search algorithms, potentially increasing both the effectiveness and efficiency of architecture discovery.

Significance and Contributions

Deep learning architectures greatly influence performance, yet the intricate task of selecting and optimizing them remains largely manual. This paper attempts to simplify this process through automation. The principal contributions of this work include:

1. Modular and Extensible Language:
   • The authors introduce a modular language for representing complex search spaces over architectures and hyperparameters. Its compositional nature provides significant flexibility, allowing users to express intricate architectural choices succinctly while being easy to traverse.
2. Search Algorithms:
   • A variety of model search algorithms leveraging tree-structured search spaces have been introduced, including random search, Monte Carlo Tree Search (MCTS), and Sequential Model-Based Optimization (SMBO). These algorithms systematically explore the search space to identify high-performing architectures.
3. Experimental Validation:
   • Experiments conducted on CIFAR-10 demonstrate MCTS and SMBO outperform random search, suggesting the efficiency of structured search algorithms in model discovery.

Numerical Results and Claims

The experiments reveal that structured model search algorithms, such as MCTS and SMBO, substantially outperform random search in identifying high-performing models in the given search space. Specifically, they yield higher model accuracy after fewer evaluations compared to random search. This strongly implies that taking advantage of the structure of the search spaces results in more effective generalization and exploration across different model designs.

Implications and Future Directions

The implications of DeepArchitect are both practical and theoretical. Practically, it provides a robust tool for the automated discovery of competitive models in less time and with reduced human effort. Additionally, it presents an opportunity for researchers to incorporate inductive biases and computational constraints seamlessly into their architectures.

Theoretically, the paper opens avenues for further refinement of search spaces and algorithms. It encourages the exploration of more sophisticated surrogate models and feature spaces. As computational power continues to grow, leveraging such frameworks will be crucial to unlocking increasingly complex architectures and learning tasks.

In the future, we might witness holistic integration with existing frameworks like Tensorflow or PyTorch, competing towards an ecosystem that automatically optimizes both model architecture and training paradigms. Such advancements will invariably propel AI researchers towards novel applications and new benchmarks.

In summary, DeepArchitect offers a promising methodology for automating deep learning architecture design, showing significant promise in eliminating the onerous manual tuning process. Continued work in this domain stands to revolutionize how researchers approach deep learning model architecture optimization.