ICDARTS: Improving the Stability and Performance of Cyclic DARTS

This work introduces improvements to the stability and generalizability of Cyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently. This training protocol aims to optimize the search process by enforcing that the search and evaluation networks produce similar outputs. However, CDARTS introduces a loss function for the evaluation network that is dependent on the search network. The dissimilarity between the loss functions used by the evaluation networks during the search and retraining phases results in a search-phase evaluation network that is a sub-optimal proxy for the final evaluation network that is utilized during retraining. We present ICDARTS, a revised approach that eliminates the dependency of the evaluation network weights upon those of the search network, along with a modified process for discretizing the search network's \textit{zero} operations that allows these operations to be retained in the final evaluation networks. We pair the results of these changes with ablation studies on ICDARTS' algorithm and network template. Finally, we explore methods for expanding the search space of ICDARTS by expanding its operation set and exploring alternate methods for discretizing its continuous search cells. These experiments resulted in networks with improved generalizability and the implementation of a novel method for incorporating a dynamic search space into ICDARTS.

The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations

Authorship analysis is an important subject in the field of natural language processing. It allows the detection of the most likely writer of articles, news, books, or messages. This technique has multiple uses in tasks related to authorship attribution, detection of plagiarism, style analysis, sources of misinformation, etc. The focus of this paper is to explore the limitations and sensitiveness of established approaches to adversarial manipulations of inputs. To this end, and using those established techniques, we first developed an experimental frame-work for author detection and input perturbations. Next, we experimentally evaluated the performance of the authorship detection model to a collection of semantic-preserving adversarial perturbations of input narratives. Finally, we compare and analyze the effects of different perturbation strategies, input and model configurations, and the effects of these on the author detection model.