publications

2021

1. Polymatrix Competitive Gradient Descent

   Jeffrey Ma, Alistair Letcher , Florian Schäfer , and 2 more authors

   Nov 2021

   Abs arXiv

   Many economic games and machine learning approaches can be cast as competitive optimization problems where multiple agents are minimizing their respective objective function, which depends on all agents’ actions. While gradient descent is a reliable basic workhorse for single-agent optimization, it often leads to oscillation in competitive optimization. In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents. The updates of our method are obtained as the Nash equilibria of a local polymatrix approximation with a quadratic regularization, and can be computed efficiently by solving a linear system of equations. We prove local convergence of PCGD to stable fixed points for n-player general-sum games, and show that it does not require adapting the step size to the strength of the player-interactions. We use PCGD to optimize policies in multi-agent reinforcement learning and demonstrate its advantages in Snake, Markov soccer and an electricity market game. Agents trained by PCGD outperform agents trained with simultaneous gradient descent, symplectic gradient adjustment, and extragradient in Snake and Markov soccer games and on the electricity market game, PCGD trains faster than both simultaneous gradient descent and the extragradient method.

2020

1. Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

   Jeffrey Ma, Ukash Nakarmi , Cedric Yue Sik Kin , and 6 more authors

   Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), May 2020

   Abs arXiv HTML

   Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.

2. Analysis of Deep Learning models for Diagnostic Image Quality Assessment in Magnetic Resonance Imaging

   Jeffrey Ma, Ukash Nakarmi , Cedric Yue Sik Kin , and 6 more authors

   Proceedings of the 2020 28th International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, Aug 2020

   Abs HTML

   In this abstract we investigate deep learning frameworks for medical image quality assessment and automatic classification of diagnostic and non-diagnostic quality images.

3. Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses

   Anish Nag , Nick Haber , Catalin Voss , and 9 more authors

   Journal of Medical Internet Research (JMIR), Apr 2020

   Abs HTML

   Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings.