Publications

Noised Consistency Training for Text Summarization

J. Y. Liu

Qianren Mao

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… (voir plus)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 (prépublication)

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 … (voir plus)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 (prépublication)

arxiv.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 (publié)

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… (voir plus)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 (prépublication)

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)… (voir plus). 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 (inconnu)

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 … (voir plus)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 (publié)

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… (voir plus)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

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

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 (publié)

doi.org

arxiv.org

Individual Fairness in Kidney Exchange Programs

Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a dono… (voir plus)r which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective of maximizing the total number of transplants. Since selecting one optimal solution translates to a decision on who receives a transplant, it has a major effect on the lives of patients. The current practice in selecting an optimal solution does not necessarily ensure fairness in the selection process. In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. We propose the use of randomized policies for selecting an optimal solution in which patients' equal opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. The advantages of our proposed method over the common practice of using the optimal solution obtained by a solver are stressed through computational experiments. Our methodology enables decision makers to fully control KEP outcomes, overcoming any potential bias or vulnerability intrinsic to a deterministic solver.

2021-05-17

AAAI Conference on Artificial Intelligence (publié)

doi.org

Learning Intuitive Physics with Multimodal Generative Models

Sahand Rezaei-Shoshtari

Francois Hogan

M. Jenkin

David Meger

Gregory Dudek

Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelli… (voir plus)gent and anticipatory actions. This paper presents a perception framework that fuses visual and tactile feedback to make predictions about the expected motion of objects in dynamic scenes. Visual information captures object properties such as 3D shape and location, while tactile information provides critical cues about interaction forces and resulting object motion when it makes contact with the environment. Utilizing a novel See-Through-your-Skin (STS) sensor that provides high resolution multimodal sensing of contact surfaces, our system captures both the visual appearance and the tactile properties of objects. We interpret the dual stream signals from the sensor using a Multimodal Variational Autoencoder (MVAE), allowing us to capture both modalities of contacting objects and to develop a mapping from visual to tactile interaction and vice-versa. Additionally, the perceptual system can be used to infer the outcome of future physical interactions, which we validate through simulated and real-world experiments in which the resting state of an object is predicted from given initial conditions.

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Meta-learning framework with applications to zero-shot time-series forecasting

Boris Oreshkin

Dmitri Carpov

Nicolas Chapados

Yoshua Bengio

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Metrics and continuity in reinforcement learning

Charline Le Lan

Bellemare Marc-Emmanuel

Pablo Samuel Castro

2021-05-17

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org