Portrait of Shenyang Huang

Shenyang Huang

huangshe@mila.quebec
Collaborating researcher - McGill University
Supervisor
Reihaneh Rabbany
Co-supervisor
Guillaume Rabusseau
Research Topics
Data Mining
Deep Learning
Graph Neural Networks
Molecular Modeling
Spectral Learning
Website
Google Scholar
GitHub

Publications

Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies
Abby Leung
Xiaoye Ding
Shenyang Huang
Reihaneh Rabbany
The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that t… (see more)he structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This model becomes similar to the classical compartment-based models in epidemiology if we assume the contact network is a Erdos-Renyi (ER) graph, i.e. everyone comes into contact with everyone else with the same probability. In contrast, CGEM is more expressive and allows for plugging in the actual contact networks, or more realistic proxies for it. Moreover, CGEM enables more precise modelling of enforcing and releasing different non-pharmaceutical intervention (NPI) strategies. Through a set of extensive experiments, we demonstrate significant differences between the epidemic curves when assuming different underlying structures. More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening.
2020-10-05
ArXiv (preprint)
arxiv.org
arxiv.org
Laplacian Change Point Detection for Dynamic Graphs
Shenyang Huang
Yasmeen Hitti
Guillaume Rabusseau
Reihaneh Rabbany
2020-08-19
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (published)
doi.org
arxiv.org
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Stefano Alletto
Shenyang Huang
Vincent François-Lavet
Yohei Nakata
Guillaume Rabusseau
Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it fin… (see more)ds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the amount of human intervention required to deploy these methods into real world scenarios. Based on our proposed evaluation checklist, we study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision. We show that our proposed random search strategy performs close to the state of the art on the AV-MNIST dataset while meeting the desirable characteristics for a fully automated design process.
2020-03-01
ArXiv (preprint)
arxiv.org
arxiv.org
Neural Architecture Search for Class-incremental Learning
Shenyang Huang
Vincent François-Lavet
Guillaume Rabusseau
In class-incremental learning, a model learns continuously from a sequential data stream in which new classes occur. Existing methods often … (see more)rely on static architectures that are manually crafted. These methods can be prone to capacity saturation because a neural network's ability to generalize to new concepts is limited by its fixed capacity. To understand how to expand a continual learner, we focus on the neural architecture design problem in the context of class-incremental learning: at each time step, the learner must optimize its performance on all classes observed so far by selecting the most competitive neural architecture. To tackle this problem, we propose Continual Neural Architecture Search (CNAS): an autoML approach that takes advantage of the sequential nature of class-incremental learning to efficiently and adaptively identify strong architectures in a continual learning setting. We employ a task network to perform the classification task and a reinforcement learning agent as the meta-controller for architecture search. In addition, we apply network transformations to transfer weights from previous learning step and to reduce the size of the architecture search space, thus saving a large amount of computational resources. We evaluate CNAS on the CIFAR-100 dataset under varied incremental learning scenarios with limited computational power (1 GPU). Experimental results demonstrate that CNAS outperforms architectures that are optimized for the entire dataset. In addition, CNAS is at least an order of magnitude more efficient than naively using existing autoML methods.
2019-09-13
ArXiv (preprint)
arxiv.org
arxiv.org