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Breandan Considine

Alumni

Publications

Idiolect: A Reconfigurable Voice Coding Assistant
Breandan Considine
Nicholas Albion
Xujie Si
This paper presents Idiolect, an open source 1 IDE plugin for voice coding and a novel approach to building bots that allows for users to de… (voir plus)fine custom commands on-the-fly. Unlike traditional chatbots, Idiolect does not pretend to be an omniscient virtual assistant but rather a reconfigurable voice programming system that empowers users to create their own commands and actions dynamically, without rebuilding or restarting the application. We offer an experience report describing the tool itself, illustrate some example use cases, and reflect on several lessons learned during the tool’s development.
2023-05-20
2023 IEEE/ACM 5th International Workshop on Bots in Software Engineering (BotSE) (publié)
doi.org
arxiv.org
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Isabela Albuquerque
Joao Monteiro
Thang Doan
Breandan Considine
T. Falk
Ioannis Mitliagkas
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in c… (voir plus)ontrast to the traditional game involving one generator against a single adversary. Such methods perform single-objective optimization on some simple consolidation of the losses, e.g. an arithmetic average. In this work, we revisit the multiple-discriminator setting by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem. Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets. Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction can be computed efficiently. Our results indicate that hypervolume maximization presents a better compromise between sample quality and computational cost than previous methods.
2019-01-01
ICML (publié)
arxiv.org
arxiv.org