Publications

Noised Consistency Training for Text Summarization
J. Y. Liu
Qianren Mao
Bang Liu
Hao Peng
Hongdong Zhu
Jian-Xin Li
Neural abstractive summarization methods often require large quantities of labeled training data. However, labeling large amounts of summari… (see more)zation data is often prohibitive due to time, financial, and expertise constraints, which has limited the usefulness of summarization systems to practical applications. In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus. The consistency regularization semi-supervised learning can regularize model predictions to be invariant to small noise applied to input articles. By adding noised unlabeled corpus to help regularize consistency training, this framework obtains comparative performance without using the full dataset. In particular, we have verified that leveraging large amounts of unlabeled data decently improves the performance of supervised learning over an insufficient labeled dataset.
2021-05-27
ArXiv (preprint)
arxiv.org
arxiv.org
AndroidEnv: A Reinforcement Learning Platform for Android
Daniel Toyama
Philippe Hamel
Anita Gergely
Gheorghe Comanici
Amelia Glaese
Zafarali Ahmed
Tyler Jackson
Shibl Mourad
Doina Precup
We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv … (see more)allows RL agents to interact with a wide variety of apps and services commonly used by humans through a universal touchscreen interface. Since agents train on a realistic simulation of an Android device, they have the potential to be deployed on real devices. In this report, we give an overview of the environment, highlighting the significant features it provides for research, and we present an empirical evaluation of some popular reinforcement learning agents on a set of tasks built on this platform.
2021-05-26
ArXiv (preprint)
arxiv.org
arxiv.org
Robotic Object Manipulation with Full-Trajectory GAN-Based Imitation Learning
Haoxu Wang
David Meger
This paper develops a novel generative imitation learning system capable of capturing the distribution of expert demonstrations in trajector… (see more)y space, which allows longer temporal context within complex motion sequences to be captured. While auto-regressive models that model time-steps sequentially can in principle be recursively applied to capture long sequences, there are known issues with learning such models reliably. In contrast, our model represents full trajectories a first-class entities, which has required us to adapt the typical generative adversarial learning architecture. We pair a full-trajectory discriminator with an imitation-inspired generative trajectory model and train these two in adversarial fashion. Our results show that our method matches the performance of existing approaches for simple tasks, in simulation and on real robot deployments. We produce state-of-the-art accuracy in replicating motions that contain long-term dependencies such as pouring.
2021-05-25
2021 18th Conference on Robots and Vision (CRV) (published)
doi.org
Publisher Correction: The default network of the human brain is associated with perceived social isolation
R. Nathan Spreng
Emile Dimas
Laetitia Mwilambwe-Tshilobo
Alain Dagher
Philipp Koellinger
Gideon Nave
Anthony Ong
Julius M. Kernbach
Thomas V. Wiecki
Tian Ge
Yue Li
Avram J. Holmes
B. T. Thomas Yeo
Gary R. Turner
Robin I. M. Dunbar
Danilo Bzdok
2021-05-20
Nature Communications (published)
doi.org
Periodic Freight Demand Estimation for Large-scale Tactical Planning
Greta Laage
Emma Frejinger
Gilles Savard
Freight carriers rely on tactical planning to design their service network to satisfy demand in a cost-effective way. For computational trac… (see more)tability, deterministic and cyclic Service Network Design (SND) formulations are used to solve large-scale problems. A central input is the periodic demand, that is, the demand expected to repeat in every period in the planning horizon. In practice, demand is predicted by a time series forecasting model and the periodic demand is the average of those forecasts. This is, however, only one of many possible mappings. The problem consisting in selecting this mapping has hitherto been overlooked in the literature. We propose to use the structure of the downstream decision-making problem to select a good mapping. For this purpose, we introduce a multilevel mathematical programming formulation that explicitly links the time series forecasts to the SND problem of interest. The solution is a periodic demand estimate that minimizes costs over the tactical planning horizon. We report results in an extensive empirical study of a large-scale application from the Canadian National Railway Company. They clearly show the importance of the periodic demand estimation problem. Indeed, the planning costs exhibit an important variation over different periodic demand estimates and using an estimate different from the mean forecast can lead to substantial cost reductions. Moreover, the costs associated with the periodic demand estimates based on forecasts were comparable to, or even better than those obtained using the mean of actual demand.
2021-05-18
ArXiv (preprint)
arxiv.org
arxiv.org
Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative
Charlene Esteban Ronquillo
Laura-Maria Peltonen
Lisiane Pruinelli
Charlene H. Chu
Suzanne Bakken
Ana Beduschi
Kenrick Cato
Nicholas Hardiker
Alain Junger
Martin Michalowski
Rune Nyrup
Samira Rahimi
Donald Nigel Reed
Tapio Salakoski
Sanna Salanterä
Nancy Walton
Patrick Weber
Thomas Wiegand
Maxim Topaz
To develop a consensus paper on the central points of an international invitational think‐tank on nursing and artificial intelligence (AI)… (see more). We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3‐day invitational think tank in autumn 2019. Activities included a pre‐event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities. Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice. There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems. We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.
2021-05-17
Journal of Advanced Nursing (unknown)
doi.org
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
Matt Grenander
Robert Belfer
Ekaterina Kochmar
Iulian V. Serban
Franccois St-Hilaire
Jackie CK Cheung
We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect … (see more)concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Tong Che
Xiaofeng Liu
Site Li
Yubin Ge
Ruixiang Zhang
Caiming Xiong
Yoshua Bengio
AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an importan… (see more)t natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework --- deep verifier networks (DVN) to detect unreliable inputs or predictions of deep discriminative models, using separately trained deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints to separate the label information from the latent representation. We give both intuitive and theoretical justifications for the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on both out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.
2021-05-17
AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
DIBS: Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha
Homanga Bharadhwaj
Anirudh Goyal
Hugo Larochelle
Animesh Garg
Florian Shkurti
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Evaluation of a prenatal screening decision aid: A mixed methods pilot study.
Titilayo Tatiana Agbadje
S. A. Rahimi
Mélissa Côté
Andrée-Anne Tremblay
Mariama Penda Diallo
Hélène Elidor
Alex Poulin Herron
Codjo Djignefa Djade
France Légaré
2021-05-17
Patient Education and Counseling (published)
doi.org
Fast and Slow Learning of Recurrent Independent Mechanisms
Kanika Madan
Rosemary Nan Ke
Anirudh Goyal
Bernhard Schölkopf
Yoshua Bengio
Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning age… (see more)nt interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.
2021-05-17
PolyPublie (accepted)
doi.org
openreview.net
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Vikas Verma
Meng Qu
Kenji Kawaguchi
Alex Lamb
Yoshua Bengio
Juho Kannala
Jian Tang
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to tra… (see more)in a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization. Further, we provide a theoretical analysis of how GraphMix improves the generalization bounds of the underlying graph neural network, without making any assumptions about the "aggregation" layer or the depth of the graph neural networks. We experimentally validate this analysis by applying GraphMix to various architectures such as Graph Convolutional Networks, Graph Attention Networks and Graph-U-Net. Despite its simplicity, we demonstrate that GraphMix can consistently improve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets: Cora-Full, Co-author-CS and Co-author-Physics.
2021-05-17
AAAI Conference on Artificial Intelligence (published)
doi.org
openreview.net