Publications

Metrics Reloaded - A new recommendation framework for biomedical image analysis validation
Annika Reinke
Lena Maier-Hein
Evangelia Christodoulou
Ben Glocker
Patrick Scholz
Fabian Isensee
Jens Kleesiek
Michal Kozubek
Mauricio Reyes
Michael Alexander Riegler
Manuel Wiesenfarth
Michael Baumgartner
Matthias Eisenmann
DOREEN HECKMANN-NÖTZEL
Ali Emre Kavur
TIM RÄDSCH
Minu D. Tizabi
Laura Acion
Michela Antonelli
Tal Arbel … (voir 48 de plus)
Spyridon Bakas
Peter Bankhead
Arriel Benis
M. Jorge Cardoso
Veronika Cheplygina
Beth A Cimini
Gary S. Collins
Keyvan Farahani
Bram van Ginneken
Fred A Hamprecht
Daniel A. Hashimoto
Michael M. Hoffman
Merel Huisman
Pierre Jannin
Charles Kahn
Alexandros Karargyris
Alan Karthikesalingam
Hannes Kenngott
Annette Kopp-Schneider
Anna Kreshuk
Tahsin Kurc
Bennett Landman
GEERT LITJENS
Amin Madani
Klaus Maier-Hein
Anne Martel
Peter Mattson
Erik Meijering
Bjoern Menze
David Moher
Karel G.M. Moons
Henning Müller
Brennan Nichyporuk
Felix Nickel
Jens Petersen
Nasir Rajpoot
Nicola Rieke
Julio Saez-Rodriguez
Clara I. Sánchez
Shravya Shetty
Maarten van Smeden
Carole H. Sudre
Ronald M. Summers
Abdel A. Taha
Sotirios A. Tsaftaris
Ben Van Calster
Gael Varoquaux
Paul F Jaeger
Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There ar… (voir plus)e major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.
2022-05-08
MIDL.io/2022/Conference/Short (accepté)
openreview.net
openreview.net
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.
2022-05-08
2022 IEEE 38th International Conference on Data Engineering (ICDE) (publié)
doi.org
arxiv.org
From inter‐brain connectivity to inter‐personal psychiatry
Guillaume Dumas
2022-05-06
World Psychiatry (publié)
doi.org
Social Neuro AI: Social Interaction as the "Dark Matter" of AI
Samuele Bolotta
Guillaume Dumas
This article introduces a three-axis framework indicating how AI can be informed by biological examples of social learning mechanisms. We ar… (voir plus)gue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. However, the field of AI has mostly embraced a solipsistic perspective on intelligence. We thus argue that social interactions not only are largely unexplored in this field but also are an essential element of advanced cognitive ability, and therefore constitute metaphorically the dark matter of AI. In the first section, we discuss how social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and empirical findings from social neuroscience. Then, we discuss three lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by dynamical systems. Third, embodiment has been demonstrated to provide more sophisticated array of communicative signals. To conclude, we discuss the example of active inference, which offers powerful insights for developing agents that possess biological realism, can self-organize in time, and are socially embodied.
2022-05-05
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
ABSTRACT Health disparity across layers of society involves reasons beyond the healthcare system. Socioeconomic status (SES) shapes people… (voir plus)s daily interaction with their social environment and is known to impact various health outcomes. Using generative probabilistic modeling, we investigate health satisfaction and complementary indicators of socioeconomic lifestyle in the human social brain. In a population cohort of ~10,000 UK Biobank participants, our first analysis probed the relationship between health status and subjective social standing (i.e., financial satisfaction). We identified volume effects in participants unhappy with their health in regions of the higher associative cortex, especially the dorsomedial prefrontal cortex (dmPFC) and bilateral temporo-parietal junction (TPJ). Specifically, participants in poor subjective health showed deviations in dmPFC and TPJ volume as a function of financial satisfaction. The second analysis on health status and objective social standing (i.e., household income) revealed volume deviations in regions of the limbic system for individuals feeling unhealthy. In particular, low-SES participants dissatisfied with their health showed deviations in volume distributions in the amygdala and hippocampus bilaterally. Thus, our population-level evidence speaks to the possibility that health status and socioeconomic position have characteristic imprints in social brain differentiation.
2022-05-03
Social Neuroscience (inconnu)
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.
2022-05-02
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
Capacity Variation in the Many-to-one Stable Matching
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Alfredo Torrico
2022-05-02
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 René Falet
Joshua Durso-finley
Brennan Nichyporuk
Julien Schroeter
Francesca Bovis
Maria-Pia Sormani
Doina Precup
Tal Arbel
Douglas Arnold
2022-05-02
Neurology (inconnu)
doi.org
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani
Olivier Bachem
Simone Totaro
Robert Müller
Shivam Garg
Matthieu Geist
Marlos C. Machado
Pablo Samuel Castro
Nicolas Le Roux
Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions… (voir plus) have been proposed, most without strong theoretical guarantees, leading to algorithms such as TRPO, PPO or MPO. Rather than design yet another surrogate function, we instead propose a general framework (FMA-PG) based on functional mirror ascent that gives rise to an entire family of surrogate functions. We construct surrogate functions that enable policy improvement guarantees, a property not shared by most existing surrogate functions. Crucially, these guarantees hold regardless of the choice of policy parameterization. Moreover, a particular instantiation of FMA-PG recovers important implementation heuristics (e.g., using forward vs reverse KL divergence) resulting in a variant of TRPO with additional desirable properties. Via experiments on simple bandit problems, we evaluate the algorithms instantiated by FMA-PG. The proposed framework also suggests an improved variant of PPO, whose robustness and efficiency we empirically demonstrate on the MuJoCo suite.
2022-05-02
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2022-04-30
Discrete Optimization (publié)
doi.org
Characterizing Idioms: Conventionality and Contingency
Michaela Socolof
Jackie Chi Kit Cheung
Michael Wagner
Timothy J. O'Donnell
Idioms are unlike most phrases in two important ways. First, the words in an idiom have non-canonical meanings. Second, the non-canonical me… (voir plus)anings of words in an idiom are contingent on the presence of other words in the idiom. Linguistic theories differ on whether these properties depend on one another, as well as whether special theoretical machinery is needed to accommodate idioms. We define two measures that correspond to the properties above, and we implement them using BERT (Devlin et al., 2019) and XLNet(Yang et al., 2019). We show that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. Our results suggest that special machinery to handle idioms may not be warranted.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
arxiv.org
Compositional Generalization in Dependency Parsing
Emily Goodwin
Siva Reddy
Timothy J. O'Donnell
Dzmitry Bahdanau
Compositionality -- the ability to combine familiar units like words into novel phrases and sentences -- has been the focus of intense inter… (voir plus)est in artificial intelligence in recent years. To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ). This dataset maximizes the similarity between the test and train distributions over primitive units, like words, while maximizing the compound divergence: the dissimilarity between test and train distributions over larger structures, like phrases. Dependency parsing, however, lacks a compositional generalization benchmark. In this work, we introduce a gold-standard set of dependency parses for CFQ, and use this to analyze the behavior of a state-of-the art dependency parser (Qi et al., 2020) on the CFQ dataset. We find that increasing compound divergence degrades dependency parsing performance, although not as dramatically as semantic parsing performance. Additionally, we find the performance of the dependency parser does not uniformly degrade relative to compound divergence, and the parser performs differently on different splits with the same compound divergence. We explore a number of hypotheses for what causes the non-uniform degradation in dependency parsing performance, and identify a number of syntactic structures that drive the dependency parser's lower performance on the most challenging splits.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
arxiv.org