Publications

Aberrant functional brain network organization is associated with relapse during 1‐year follow‐up in alcohol‐dependent patients
Justin Böhmer
Pablo Reinhardt
Maria Garbusow
Michael Marxen
Michael N. Smolka
Ulrich S. Zimmermann
Andreas Heinz
Danilo Bzdok
Eva Friedel
Johann D. Kruschwitz
Henrik Walter
2023-10-02
Addiction Biology (published)
doi.org
GraphText: Graph Reasoning in Text Space
Jianan Zhao
Le Zhuo
Yikang Shen
Meng Qu
Kai Liu
Michael Bronstein
Zhaocheng Zhu
Jian Tang
Large Language Models (LLMs) have gained the ability to assimilate human knowledge and facilitate natural language interactions with both hu… (see more)mans and other LLMs. However, despite their impressive achievements, LLMs have not made significant advancements in the realm of graph machine learning. This limitation arises because graphs encapsulate distinct relational data, making it challenging to transform them into natural language that LLMs understand. In this paper, we bridge this gap with a novel framework, GraphText, that translates graphs into natural language. GraphText derives a graph-syntax tree for each graph that encapsulates both the node attributes and inter-node relationships. Traversal of the tree yields a graph text sequence, which is then processed by an LLM to treat graph tasks as text generation tasks. Notably, GraphText offers multiple advantages. It introduces training-free graph reasoning: even without training on graph data, GraphText with ChatGPT can achieve on par with, or even surpassing, the performance of supervised-trained graph neural networks through in-context learning (ICL). Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language. These capabilities underscore the vast, yet-to-be-explored potential of LLMs in the domain of graph machine learning.
2023-10-02
ArXiv (preprint)
doi.org
arxiv.org
Imitation Learning from Observation through Optimal Transport
Wei-Di Chang
Scott Fujimoto
David Meger
Gregory Dudek
2023-10-02
ArXiv (preprint)
doi.org
arxiv.org
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
Arjun Ashok
Étienne Marcotte
Valentina Zantedeschi
Nicolas Chapados
Alexandre Drouin
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (see more)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.
2023-10-02
ArXiv (preprint)
doi.org
arxiv.org
AI and Catastrophic Risk
Yoshua Bengio
2023-10-01
Journal of Democracy (published)
doi.org
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning
Jensen Gao
Siddharth Reddy
Glen Berseth
Anca Dragan
Sergey Levine
Adaptive interfaces can help users perform sequential decision-making tasks like robotic teleoperation given noisy, high-dimensional command… (see more) signals (e.g., from a brain-computer interface). Recent advances in human-in-the-loop machine learning enable such systems to improve by interacting with users, but tend to be limited by the amount of data that they can collect from individual users in practice. In this paper, we propose a reinforcement learning algorithm to address this by training an interface to map raw command signals to actions using a combination of offline pre-training and online fine-tuning. To address the challenges posed by noisy command signals and sparse rewards, we develop a novel method for representing and inferring the user's long-term intent for a given trajectory. We primarily evaluate our method's ability to assist users who can only communicate through noisy, high-dimensional input channels through a user study in which 12 participants performed a simulated navigation task by using their eye gaze to modulate a 128-dimensional command signal from their webcam. The results show that our method enables successful goal navigation more often than a baseline directional interface, by learning to denoise user commands signals and provide shared autonomy assistance. We further evaluate on a simulated Sawyer pushing task with eye gaze control, and the Lunar Lander game with simulated user commands, and find that our method improves over baseline interfaces in these domains as well. Extensive ablation experiments with simulated user commands empirically motivate each component of our method.
2023-10-01
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)
doi.org
arxiv.org
Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis.
Pedro Vianna
Sara-Ivana Calce
Pamela Boustros
Cassandra Larocque-Rigney
Laurent Patry-Beaudoin
Yi Hui Luo
Emre Aslan
John Marinos
Talal M. Alamri
Kim-Nhien Vu
Jessica Murphy-Lavallée
Jean-Sébastien Billiard
Emmanuel Montagnon
Hongliang Li
Samuel Kadoury
Bich Nguyen
Shanel Gauthier
Benjamin Thérien
Irina Rish
Eugene Belilovsky … (see 4 more)
Guy Wolf
Michael Chassé
Guy Cloutier
An Tang
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose T… (see more)o evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.
2023-10-01
Radiology (published)
doi.org
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
Ahmed Haj Yahmed
Altaf Allah Abbassi
Amin Nikanjam
Heng Li
Foutse Khomh
Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement learning, has shown significant potential in achieving hum… (see more)an-level autonomy in a wide range of domains, including robotics, computer vision, and computer games. This potential justifies the enthusiasm and growing interest in DRL in both academia and industry. However, the community currently focuses mostly on the development phase of DRL systems, with little attention devoted to DRL deployment. In this paper, we propose an empirical study on Stack Overflow (SO), the most popular Q&A forum for developers, to uncover and understand the challenges practitioners faced when deploying DRL systems. Specifically, we categorized relevant SO posts by deployment platforms: server/cloud, mobile/embedded system, browser, and game engine. After filtering and manual analysis, we examined 357 SO posts about DRL deployment, investigated the current state, and identified the challenges related to deploying DRL systems. Then, we investigate the prevalence and difficulty of these challenges. Results show that the general interest in DRL deployment is growing, confirming the study’s relevance and importance. Results also show that DRL deployment is more difficult than other DRL issues. Additionally, we built a taxonomy of 31 unique challenges in deploying DRL to different platforms. On all platforms, RL environment-related challenges are the most popular, and communication-related challenges are the most difficult among practitioners. We hope our study inspires future research and helps the community overcome the most common and difficult challenges practitioners face when deploying DRL systems.
2023-10-01
2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) (published)
doi.org
arxiv.org
An Empirical Study on Bugs Inside PyTorch: A Replication Study
Vahid Majdinasab
Sharon Chee Yin Ho
Mohayeminul Islam
Diego Elias Costa
Emad Shihab
Foutse Khomh
Sarah Nadi
Muhammad Raza
Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patter… (see more)ns and powering intelligent behaviour. A core enabler of this change in software development is the availability of easy-to-use deep learning libraries. Libraries like PyTorch and TensorFlow empower a large variety of intelligent systems, offering a multitude of algorithms and configuration options, applicable to numerous domains of systems. However, bugs in those popular deep learning libraries also may have dire consequences for the quality of systems they enable; thus, it is important to understand how bugs are identified and fixed in those libraries.Inspired by a study of Jia et al., which investigates the bug identification and fixing process at TensorFlow, we characterize bugs in the PyTorch library, a very popular deep learning framework. We investigate the causes and symptoms of bugs identified during PyTorch’s development, and assess their locality within the project, and extract patterns of bug fixes. Our results highlight that PyTorch bugs are more like traditional software projects bugs, than related to deep learning characteristics. Finally, we also compare our results with the study on TensorFlow, highlighting similarities and differences across the bug identification and fixing process.
2023-10-01
2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) (published)
doi.org
arxiv.org
F66. FROM GENE TO COGNITION: MAPPING THE EFFECTS OF GENOMIC DELETIONS AND DUPLICATIONS ON COGNITIVE ABILITY
Sayeh Kazem
Kuldeep Kumar
Guillaume Huguet
Myriam Lizotte
Thomas Renne
Jakub Kopal
Stefan Horoi
Martineau Jean-Louis
Zohra Saci
Laura Almasy
David C. Glahn
Guy Wolf
Guillaume Dumas
Sébastien Jacquemont
2023-10-01
European Neuropsychopharmacology (published)
doi.org
A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Volker Strobel
Andreagiovanni Reina
Xue (Steve) Liu
Gregory Dudek
Marco Dorigo
Recent studies show that some security features that blockchains grant to decentralized networks on the internet can be ported to swarm robo… (see more)tics. Although the integration of blockchain technology and swarm robotics shows great promise, thus far, research has been limited to proof-of-concept scenarios where the blockchain-based mechanisms are tailored to a particular swarm task and operating environment. In this study, we propose a generic framework based on a blockchain smart contract that enables robot swarms to achieve secure consensus in an arbitrary observation space. This means that our framework can be customized to fit different swarm robotics missions, while providing methods to identify and neutralize Byzantine robots, that is, robots which exhibit detrimental behaviours stemming from faults or malicious tampering.
2023-10-01
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)
doi.org
Going beyond the means: Exploring the role of bias from digital determinants of health in technologies
Marie-Laure Charpignon
Adrien Carrel
Yihang Jiang
Teddy Kwaga
Beatriz Cantada
Terry Hyslop
Christopher E. Cox
Krista Haines
Valencia Koomson
Guillaume Dumas
Michael Morley
Jessilyn Dunn
An-Kwok Ian Wong
2023-10-01
PLOS Digital Health (published)
doi.org