Publications

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
Jiayuan Ding
Tong Xiang
Zijing Ou
Wangyang Zuo
Ruihui Zhao
Chenhua Lin
Yefeng Zheng
Bang Liu
Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the a… (voir plus)rea of computer science. How to glean papers worth reading from the massive literature to do a quick survey or keep up with the latest advancement about a specific research topic has become a challenging task. Existing academic search engines return relevant papers by individually calculating the relevance between each paper and query. However, such systems usually omit the prerequisite chains of a research topic and cannot form a meaningful reading path. In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. To serve as a research benchmark, we further propose SurveyBank, a dataset consisting of large quantities of survey papers in the field of computer science as well as their citation relationships. Furthermore, we propose a graph-optimization-based approach for reading path generation which takes the relationship between papers into account. Extensive evaluations demonstrate that our approach outperforms other baselines. A real-time Reading Path Generation (RePaGer) system has been also implemented with our designed model. Our source code and SurveyBank dataset can be found here11https://github.com/JiayuanDing100/Reading-Path-Generation.
2022-05-09
2022 IEEE 38th International Conference on Data Engineering (ICDE) (publié)
doi.org
arxiv.org
From inter‐brain connectivity to inter‐personal psychiatry
Guillaume Dumas
2022-05-07
World Psychiatry (publié)
doi.org
Social Neuro AI: Social Interaction as the “Dark Matter” of AI
Samuele Bolotta
Guillaume Dumas
2022-05-06
Frontiers in Computer Science (publié)
doi.org
arxiv.org
Population variation in social brain morphology: Links to socioeconomic status and health disparity
Nathania Suryoputri
Hannah Kiesow
Danilo Bzdok
2022-05-04
Social Neuroscience (publié)
doi.org
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi
Adam Ścibior
Andreas Munk
Mehrdad Ghadiri
Atilim Güneş Baydin
Bradley Gram-Hansen
C. S. D. Witt
Robert Zinkov
Philip Torr
Tom Rainforth
Yee Whye Teh
Frank N. Wood
Existing approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. An … (voir plus)instance of this is importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we develop a new and efficient amortized importance sampling estimator. We prove finite variance of our estimator and empirically demonstrate our method's correctness and efficiency compared to existing alternatives on generative programs containing rejection sampling loops and discuss how to implement our method in a generic probabilistic programming framework.
2022-05-03
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
arxiv.org
Capacity Variation in the Many-to-one Stable Matching
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Alfredo Torrico
2022-05-03
ArXiv (prépublication)
doi.org
arxiv.org
Deep Learning Prediction of Response to Disease Modifying Therapy in Primary Progressive Multiple Sclerosis (P1-1.Virtual)
Jean-Pierre R. Falet
Joshua D. Durso-Finley
Brennan Nichyporuk
Julien Schroeter
Francesca Bovis
Maria-Pia Sormani
Doina Precup
Tal Arbel
Douglas Arnold
2022-05-03
Neurology (publié)
doi.org
(Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment
Yoshua Bengio
Prateek Arun Gupta
Dylan Radovic
Maarten P. Scholl
Andrew Robert Williams
C. S. D. Witt
Tianyu Zhang
Yang Zhang
2022-05-02
ArXiv (prépublication)
doi.org
arxiv.org
Retrieval-Enhanced Machine Learning
Hamed Zamani
Fernando Diaz
Mostafa Dehghani
Donald Metzler
Michael Bendersky
Although information access systems have long supportedpeople in accomplishing a wide range of tasks, we propose broadening the scope of use… (voir plus)rs of information access systems to include task-driven machines, such as machine learning models. In this way, the core principles of indexing, representation, retrieval, and ranking can be applied and extended to substantially improve model generalization, scalability, robustness, and interpretability. We describe a generic retrieval-enhanced machine learning (REML) framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. The REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
2022-05-02
ArXiv (preprint)
doi.org
arxiv.org
AmbieGen tool at the SBST 2022 Tool Competition
Dmytro Humeniuk
Giuliano Antoniol
Foutse Khomh
AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been… (voir plus) adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.
2022-05-01
International Workshop on Search-Based Software Testing (published)
doi.org
Challenges in Machine Learning Application Development: An Industrial Experience Report
Md. Saidur Rahman
Foutse Khomh
Emilio Martínez Rivera
Yann‐Gaël Guéhéneuc
Bernd Lehnert
SAP is the market leader in enterprise application software offering an end-to-end suite of applications and services to enable their custom… (voir plus)ers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales transactions for their day-to-day business. Transactions are created during retail sales at the point of sale (POS) terminals and those transactions are then sent to some central servers for validations and other business operations. A considerable proportion of the retail transactions may have inconsistencies or anomalies due to many technical and human errors. SAP provides an automated process for error detection but still requires a manual process by dedicated employees using workbench software for correction. However, manual corrections of these errors are time-consuming, labor-intensive, and might be prone to further errors due to incorrect modifications. Thus, automated detection and correction of transaction errors are very important regarding their potential business values and the improvement in the business workflow. In this paper, we report on our experience from a project where we develop an AI-based system to automatically detect transaction errors and propose corrections. We identify and discuss the challenges that we faced during this collaborative research and development project, from two distinct perspectives: Software Engineering and Machine Learning. We report on our experience and insights from the project with guidelines for the identified challenges. We collect developers’ feedback for qualitative analysis of our findings. We believe that our findings and recommendations can help other researchers and practitioners embarking into similar endeavours. CCS CONCEPTS • Software and its engineering → Programming teams.
2022-05-01
2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) (published)
doi.org
Challenges in Machine Learning Application Development: An Industrial Experience Report
Md Saidur Rahman
Foutse Khomh
Emilio Rivera
Yann‐Gaël Guéhéneuc
Bernd Lehnert
2022-05-01
2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) (publié)
doi.org