Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning, which is guided with the tree structure in MCTS. In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more sample efficient than Random Search and Regularized Evolution in finding the global optimum. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.Comment: To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020

Improving treebank-based automatic LFG induction for Spanish

We describe several improvements to the method of treebank-based LFG induction for Spanish from the Cast3LB treebank (O’Donovan et al., 2005). We discuss the different categories of problems encountered and present the solutions adopted. Some of the problems involve a simple adoption of existing linguistic analyses, as in our treatment of clitic doubling and null subjects. In other cases there is no standard LFG account for the phenomenon we wish to model and we adopt a compromise, conservative solution. This is exemplified by our treatment of Spanish periphrastic constructions. In yet another case, the less configurational nature of Spanish means that the LFG annotation algorithm has to rely mostly on Cast3LB function tags, and consequently a reliable method of adding those tags to parse trees had to be developed. This method achieves over 6% improvement over the baseline for the Cast3LB-function-tag assignment task, and over 3% improvement over the baseline for LFG f-structure construction from function-tag-enriched trees