Publications

FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data
Mike He Zhu
Lena Nehale Ezzine
Dianbo Liu
Yoshua Bengio
2022-05-18
ArXiv (prépublication)
doi.org
arxiv.org
IL-flOw: Imitation Learning from Observation using Normalizing Flows
Wei-Di Chang
Juan Higuera
Scott Fujimoto
David Meger
Gregory Dudek
2022-05-18
ArXiv (prépublication)
doi.org
arxiv.org
Human brain anatomy reflects separable genetic and environmental components of socioeconomic status
Hyeokmoon Kweon
Gökhan Aydogan
Alain Dagher
Danilo Bzdok
Christian C. Ruff
Gideon Nave
Martha J. Farah
Philipp D. Koellinger
Socioeconomic status (SES) correlates with brain structure, a relation of interest given the long-observed relations of SES to cognitive abi… (voir plus)lities and health. Yet, major questions remain open, in particular, the pattern of causality that underlies this relation. In an unprecedently large study, here, we assess genetic and environmental contributions to SES differences in neuroanatomy. We first establish robust SES–gray matter relations across a number of brain regions, cortical and subcortical. These regional correlates are parsed into predominantly genetic factors and those potentially due to the environment. We show that genetic effects are stronger in some areas (prefrontal cortex, insula) than others. In areas showing less genetic effect (cerebellum, lateral temporal), environmental factors are likely to be influential. Our results imply a complex interplay of genetic and environmental factors that influence the SES-brain relation and may eventually provide insights relevant to policy.
2022-05-17
Science Advances (publié)
doi.org
Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties
Courtney Paquette
Elliot Paquette
Ben Adlam
Jeffrey Pennington
2022-05-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Reconstruction of full-length LINE-1 progenitors from ancestral genomes
Laura F Campitelli
Isaac Yellan
Mihai Albu
Marjan Barazandeh
Zain M Patel
Mathieu Blanchette
Timothy R Hughes
2022-05-11
Genetics (publié)
doi.org
Undiscounted Recursive Path Choice Models: Convergence Properties and Algorithms
Tien Mai
Emma Frejinger
2022-05-11
Transportation Science (publié)
doi.org
Block Contextual MDPs for Continual Learning
Shagun Sodhani
Franziska Meier
Joelle Pineau
Amy Zhang
In reinforcement learning (RL), when defining a Markov Decision Process (MDP), the environment dynamics is implicitly assumed to be stationa… (voir plus)ry. This assumption of stationarity, while simplifying, can be unrealistic in many scenarios. In the continual reinforcement learning scenario, the sequence of tasks is another source of nonstationarity. In this work, we propose to examine this continual reinforcement learning setting through the Block Contextual MDP (BC-MDP) framework, which enables us to relax the assumption of stationarity. This framework challenges RL algorithms to handle both nonstationarity and rich observation settings and, by additionally leveraging smoothness properties, enables us to study generalization bounds for this setting. Finally, we take inspiration from adaptive control to propose a novel algorithm that addresses the challenges introduced by this more realistic BC-MDP setting, allows for zero-shot adaptation at evaluation time, and achieves strong performance on several nonstationary environments.
2022-05-10
Proceedings of The 4th Annual Learning for Dynamics and Control Conference (publié)
proceedings.mlr.press
proceedings.mlr.press
Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation
Yuyan Chen
Yanghua Xiao
Bang Liu
Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA … (voir plus)systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions.
2022-05-08
2022 IEEE 38th International Conference on Data Engineering (ICDE) (publié)
doi.org
arxiv.org
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