Publications

GAPS Phase III: incorporation of capacity based weighting in the global assessment for pediatric surgery
Yasmine Yousef
Emmanuel Ameh
Luc Malemo Kalisya
Dan Poenaru
2024-11-06
Pediatric Surgery International (publié)
doi.org
SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.
Enamundram Naga Karthik
Jan Valošek
Andrew C. Smith
Dario Pfyffer
Simon Schading-Sassenhausen
Lynn Farner
KA Weber
Kenneth A. Weber
Patrick Freund
Julien Cohen-Adad
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This ar… (voir plus)ticle will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023 from 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 males). The data consisted of T2-weighted MRI acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic and lumbar spine. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg and contrast-agnostic, all part of the Spinal Cord Toolbox). Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results SCIseg achieved a Dice score of 0.92 ± 0.07 (mean ± SD) and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and extracted relevant lesion biomarkers (namely, lesion volume, lesion length, and maximal axial damage ratio). SCIseg is open-source and accessible through the Spinal Cord Toolbox (v6.2 and above). Published under a CC BY 4.0 license.
2024-11-06
Radiology: Artificial Intelligence (publié)
doi.org
Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future
B Mark Keegan
Martina Absinta
Julien Cohen-Adad
Eoin P Flanagan
Roland G Henry
Eric C Klawiter
Shannon Kolind
Stephen Krieger
Cornelia Laule
John A Lincoln
Steven Messina
Jiwon Oh
Nico Papinutto
Seth Aaron Smith
Anthony Traboulsee
2024-11-06
Brain Communications (publié)
doi.org
Towards Optimizing SQL Generation via LLM Routing
Mohammadhossein Malekpour
Nour Shaheen
Foutse Khomh
Amine Mhedhbi
Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capabl… (voir plus)e large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset.
2024-11-06
ArXiv (prépublication)
arxiv.org
arxiv.org
GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
Nizar Islah
Justine Gehring
Diganta Misra
Eilif Muller
Irina Rish
Terry Yue Zhuo
Massimo Caccia
2024-11-05
ArXiv (prépublication)
arxiv.org
arxiv.org
Imagining and building wise machines: The centrality of AI metacognition
Samuel G. B. Johnson
Amir-Hossein Karimi
Yoshua Bengio
Nick Chater
Tobias Gerstenberg
Kate Larson
Sydney Levine
Melanie Mitchell
Iyad Rahwan
Bernhard Schölkopf
Igor Grossmann
2024-11-04
ArXiv (prépublication)
arxiv.org
arxiv.org
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
Md Rifat Arefin
Gopeshh Raaj Subbaraj
Nicolas Gontier
Yann LeCun
Irina Rish
Ravid Shwartz-Ziv
Chris Pal
2024-11-04
ArXiv (prépublication)
arxiv.org
arxiv.org
Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach
Prashant Govindarajan
Mathieu Reymond
Santiago Miret
Mariano Phielipp
Sarath Chandar
2024-11-03
NeurIPS.cc/2024/Workshop/AI4Mat (publié)
openreview.net
openreview.net
Molphenix: A Multimodal Foundation Model for PhenoMolecular Retrieval
Philip Fradkin
Puria Azadi Moghadam
Karush Suri
Frederik Wenkel
Maciej Sypetkowski
Dominique Beaini
Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellu… (voir plus)lar morphology, utilize microscopy based techniques and demonstrate a high throughput solution for uncovering molecular impact on the cell. In this work, we learn a joint latent space between molecular structures and microscopy phenomic experiments, aligning paired samples with contrastive learning. Specifically, we study the problem of Contrastive PhenoMolecular Retrieval, which consists of zero-shot molecular structure identification conditioned on phenomic experiments. We assess challenges in multi-modal learning of phenomics and molecular modalities such as experimental batch effect, inactive molecule perturbations, and encoding perturbation concentration. We demonstrate improved multi-modal learner retrieval through (1) a uni-modal pre-trained phenomics model, (2) a novel inter sample similarity aware loss, and (3) models conditioned on a representation of molecular concentration. Following this recipe, we propose MolPhenix, a molecular phenomics model. MolPhenix leverages a pre-trained phenomics model to demonstrate significant performance gains across perturbation concentrations, molecular scaffolds, and activity thresholds. In particular, we demonstrate an 8.1
2024-11-02
NeurIPS.cc/2024/Workshop/FM4Science (présentation orale)
openreview.net
openreview.net
Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not cont… (voir plus)ain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the software development process poses new challenges for code auditing. The dataset for training these models is mainly collected from publicly available sources. This raises the issue of intellectual property infringement as developers' codes are already included in the dataset. Therefore, auditing code developed using LLMs is challenging, as it is difficult to reliably assert if an LLM used during development has been trained on specific copyrighted codes, given that we do not have access to the training datasets of these models. Given the non-disclosure of the training datasets, traditional approaches such as code clone detection are insufficient for asserting copyright infringement. To address this challenge, we propose a new approach, TraWiC; a model-agnostic and interpretable method based on membership inference for detecting code inclusion in an LLM's training dataset. We extract syntactic and semantic identifiers unique to each program to train a classifier for detecting code inclusion. In our experiments, we observe that TraWiC is capable of detecting 83.87% of codes that were used to train an LLM. In comparison, the prevalent clone detection tool NiCad is only capable of detecting 47.64%. In addition to its remarkable performance, TraWiC has low resource overhead in contrast to pair-wise clone detection that is conducted during the auditing process of tools like CodeWhisperer reference tracker, across thousands of code snippets.
2024-11-02
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
arxiv.org
Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region
Armando Romani
Alberto Antonietti
Davide Bella
Julian Budd
Elisabetta Giacalone
Kerem Kurban
Sára Sáray
Marwan Abdellah
Alexis Arnaudon
Elvis Boci
Cristina Colangelo
Jean-Denis Courcol
Thomas Delemontex
András Ecker
Joanne Falck
Cyrille Favreau
Michael Gevaert
Juan B. Hernando
Joni Herttuainen
Genrich Ivaska … (voir 28 de plus)
Lida Kanari
Anna-Kristin Kaufmann
James King
Pramod Kumbhar
Sigrun Lange
Huanxiang Lu
Carmen Alina Lupascu
Rosanna Migliore
Fabien Petitjean
Judit Planas
Pranav Rai
Srikanth Ramaswamy
Michael W. Reimann
Juan Luis Riquelme
Nadir Román Guerrero
Ying Shi
Vishal Sood
Mohameth François Sy
Werner Van Geit
Liesbeth Vanherpe
Tamás F. Freund
Audrey Mercer
Eilif Benjamin Muller
Felix Schürmann
Alex M. Thomson
Michele Migliore
Szabolcs Káli
Henry Markram
The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive fun… (voir plus)ctions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions. In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings. Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.
2024-11-01
PLoS Biology (publié)
doi.org
Plot Twist: Multimodal Models Don’t Comprehend Simple Chart Details
Yasaman Razeghi
Ishita Dasgupta
Fangyu Liu
Vinay Venkatesh Ramasesh
Sameer Singh
Sebastian Mustafa
Ibrahim Goodman
Piotr Alabdul-mohsin
Daniel Padlewski
Xi Salz
Xiong Daniel
Filip Vlasic
Keran Pavetic
Tianli Rong
Yu
Wenliang Dai
Junnan Li
Dongxu Li
Anthony Meng
Huat Tiong … (voir 480 de plus)
Junqi Zhao
Weisheng Wang
Bo Li
Pascale Fung
Chaoyou Fu
Pei-Chun Chen
Yunhang Shen
Yulei Qin
Mengdan Zhang
Xu Lin
Jinrui Yang
Xiawu Zheng
Rohan Anil
Sebastian Borgeaud
Yonghui Wu
Jean-Baptiste Alayrac
Jiahui Yu
Radu Soricut
J. Schalkwyk
Andrew M. Dai
A.E. Hauth
Katie Millican
David Silver
Slav Petrov
Melvin Johnson
Ioannis Antonoglou
Julian Schrit-twieser
Amelia Glaese
Jilin Chen
Emily Pitler
Timothy P Lillicrap
Angeliki Lazaridou
Orhan Fi-rat
James L. Molloy
Michael Acheson Isard
Paul R. Barham
Tom Hennigan
Benjamin Lee
Fabio Viola
Malcolm Reynolds
Yuanzhong Xu
Ryan Doherty
Eli Collins
Clemens Meyer
Eliza Rutherford
Erica Moreira
Kareem W. Ayoub
Megha Goel
George Tucker
Enrique Piqueras
M. Krikun
Iain Barr
Nikolay Savinov
Ivo Danihelka
Becca Roelofs
Anais White
Anders Johan Andreassen
Tamara von Glehn
Laksh-man Yagati
Mehran Kazemi
Lucas Gonzalez
Misha Khalman
Jakub Sygnowski
Alexandre Fréchette
Charlotte Smith
Laura Culp
Lev Proleev
Yi Luan
Xi Chen
James Lottes
Nathan Schucher
Federico Lebron
Alban Rrustemi
Natalie Clay
Phil Crone
Tomas Kocisky
Jeffrey Zhao
Bartek Perz
Dian Yu
Heidi Howard
Adam E. Bloniarz
Jack W. Rae
Han Lu
Laurent Sifre
Marcello Maggioni
Fred Alcober
Dan Garrette
Megan Barnes
Shantanu Thakoor
Jacob Austin
Gabriel Barth-Maron
William Wong
Rishabh Joshi
Rahma Chaabouni
Deeni Fatiha
Arun Ahuja
Ruibo Liu
Yunxuan Li
Sarah Cogan
Jeremy Chen
Chao Jia
Chenjie Gu
Qiao Zhang
Ale Jordan Grimstad
Jakse Hartman
Martin Chad-wick
Gaurav Singh Tomar
Xavier Garcia
Evan Senter
Emanuel Taropa
Thanumalayan Sankaranarayana Pillai
Jacob Devlin
Michael Laskin
Diego de
Las Casas
Dasha Valter
Connie Tao
Lorenzo Blanco
Adrià Puigdomènech Badia
David Reitter
Mianna Chen
Jenny Brennan
Clara E. Rivera
Sergey Brin
Shariq N Iqbal
Gabriela Surita
Jane Labanowski
Abhishek Rao
Stephanie Winkler
Emilio Parisotto
Yiming Gu
Kate Olszewska
Yujing Zhang
Ravi Ad-danki
Antoine Miech
Annie Louis
Laurent El
Denis Teplyashin
Geoff Brown
Elliot Catt
Nithya Attaluri
Jan Balaguer
Jackie Xiang
Pidong Wang
Zoe C. Ashwood
Anton Briukhov
Albert Webson
Sanjay Ganapathy
Smit Sanghavi
Ajay Kannan
Ming-Wei Chang
Axel Stjerngren
Josip Djolonga
Yuting Sun
Ankur Bapna
Matthew Aitchison
Pedram Pejman
Henryk Michalewski
Tianhe Yu
Cindy Wang
J Christopher Love
Junwhan Ahn
Dawn Bloxwich
Kehang Han
Peter Conway Humphreys
Thibault Sellam
James Bradbury
Varun Godbole
Sina Samangooei
Bogdan Damoc
Alex Kaskasoli
S'ebastien M. R. Arnold
Vijay Vasudevan
Shubham Agrawal
Jason Riesa
Dmitry Lepikhin
Richard Tan-burn
Srivatsan Srinivasan
Hyeontaek Lim
Sarah Hodkinson
Pranav Shyam
Johan Ferret
Steven Hand
Ankush Garg
T. Paine
Jian Li
Yu-jia Li
Minh Giang
Alexander Neitz
Zaheer Abbas
Sarah York
Machel Reid
Elizabeth Cole
Aakanksha Chowdhery
Dipanjan Das
Dominika Rogozi´nska
Vitaly Nikolaev
Pablo G. Sprechmann
Zachary Nado
Lukáš Žilka
Flavien Prost
Luheng He
Marianne Monteiro
Gaurav Mishra
Christoper A. Welty
Joshua Newlan
Dawei Jia
Miltiadis Allamanis
Clara Huiyi Hu
Raoul de Liedekerke
Justin Gilmer
Carl Saroufim
Shruti Rijhwani
Shaobo Hou
Disha Shrivastava
Anirudh Baddepudi
Alex Goldin
Adnan Ozturel
Albin Cassirer
Yunhan Xu
Daniel Sohn
Deven-dra Sachan
Reinald Kim Amplayo
Craig Swan-son
Dessie Petrova
Shashi Narayan
Arthur Guez
Siddhartha Brahma
Jessica Landon
Miteyan Patel
Ruizhe Zhao
Kevin Villela
Luyu Wang
Wenhao Jia
Matthew Rahtz
Mai Giménez
Legg Yeung
Hanzhao Lin
James Keeling
Petko Georgiev
Diana Mincu
Boxi Wu
Salem Haykal
Rachel Sapu-tro
Kiran N. Vodrahalli
James Qin
Zeynep Cankara
Abhanshu Sharma
Nicholas Fernando
Will Hawkins
Behnam Neyshabur
Solomon Kim
Adrian Hut-ter
Priyanka Agrawal
Alex Castro-Ros
George van den Driessche
Tao Wang
Fan Yang
Shuo yiin
Paul Chang
Ross Komarek
Mario McIlroy
Luˇci´c Guodong
Wael Zhang
Michael Farhan
Sharman Paul
Paul Natsev
Yong Michel
Yamini Cheng
Siyuan Bansal
Kris Qiao
Siamak Cao
Shakeri Christina
Justin Butterfield
Paul Kishan Chung
Shivani Rubenstein
Arthur Agrawal
Kedar Mensch
Karel Soparkar
Timothy Lenc
Aedan Chung
Pope Loren
Jackie Maggiore
Priya Kay
Shibo Jhakra
Joshua Wang
Mary Maynez
Taylor Phuong
Tobin Andrea
Maja Tacchetti
Kevin Trebacz
Robinson Yash
Sebas-tian Katariya
Paige Riedel
Ke-fan Bailey
Nimesh Xiao
Lora Ghelani
Ambrose Aroyo
Neil Slone
Xuehan Houlsby
Zhen Xiong
Yang Elena
Jonas Gribovskaya
Mateo Adler
Lisa Wirth
M. Lee
Jay Kagohara
So-phie Pavagadhi
Anna Bridgers
Sanjay Bortsova
Ghemawat Zafarali
Tianqi Ahmed
Richard Liu
Vijay Powell
Mariko Bolina
Polina Iinuma
James Zablotskaia
Da-Woon Besley
Timothy Chung
Ramona Dozat
Xi-ance Comanescu
Jeremy Si
Guolong Greer
Su Martin
Raphaël Lopez Polacek
Simon Kaufman
Hex-iang Tokumine
Elena Hu
Yingjie Buchatskaya
Mo-hamed Miao
Aditya Elhawaty
Nenad Siddhant
Jinwei Tomasev
Christina Xing
Helen Greer
Miller Shereen
Aurko Ashraf
Zizhao Roy
Ada Zhang
Mathew Angelos
Milos Filos
Rory Besta
Ted Blevins
Kli-5927 Chih-Kuan
Soravit Yeh
Jiaqi Changpinyo
Oscar Mu
Mantas Chang
Carrie Pajarskas
Muir Vered
Charline Cohen
Krishna Le Lan
Haridasan Amit
Steven Marathe
Sholto Hansen
Ra-jkumar Douglas
Mingqiu Samuel
Sophia Wang
Austin Chang
Jiepu Lan
Justin Jiang
Jaime Alonso Chiu
Lars Lowe Lorenzo
Sébastien Sjösund
Cevey Zach
Thi Gleicher
Anudhyan Avrahami
Boral Hansa
Vittorio Srinivasan
Rhys Selo
Kon-stantinos May
Léonard Aisopos
Hussenot
Livio Baldini
Kate Soares
Michael B Baumli
Adrià Chang
Ben Caine
Alexander Pritzel
Filip Pavetic
Fabio Pardo
Anita Gergely
Justin Frye
Dan Horgan
Kartikeya Badola
Nora Kassner
Subhrajit Roy
Víctor Ethan Dyer
Alex Tomala
Yunhao Tang
Dalia El Badawy
Elspeth White
Basil Mustafa
Oran Lang
Abhishek Jindal
Sharad Mandyam Vikram
Zhitao Gong
Sergi Caelles
Ross Hemsley
Gregory Thornton
Fangxiaoyu Feng
Wojciech Stokowiec
Ce Zheng
Phoebe Thacker
Ça˘glar Ünlü
Zhishuai Zhang
Mohammad Saleh
James Svensson
Maxwell Bileschi
Piyush Patil
Ankesh Anand
Roman Ring
Katerina Tsihlas
Arpi Vezer
Marco Selvi
Toby Shevlane
Mikel Ro-driguez
Tom Kwiatkowski
Samira Daruki
Keran Rong
Allan Dafoe
Nicholas FitzGerald
Keren Gu-Lemberg
Mina Khan
Lisa Anne Hendricks
Marie Pellat
Vladimir Feinberg
James Cobon-Kerr
Tara N. Sainath
Maribeth Rauh
Sayed Hadi
Richard Hashemi
Yana Ives
YaGuang Hasson
Eric Li
Yuan Noland
Nathan Cao
Le Byrd
Qingze Hou
Thibault Wang
Michela Sottiaux
Paganini Jean-Baptiste
Alexandre Lespiau
Samer Moufarek
Kaushik Hassan
Joost Shivakumar
Amol van Amers-foort
Pratik Mandhane
Anirudh Joshi
Matthew Goyal
Andrew Tung
Hannah Brock
Vedant Shea-han
Cheng Misra
Nemanja Li
Raki´cevi´c Mostafa
Fangyu Dehghani
Sid Liu
Junhyuk Mittal
Seb Oh
Eren Noury
Fantine Sezener
Matthew Huot
Nicola Lamm
Charlie De Cao
Gamaleldin Chen
Ed Elsayed
Mahdis Chi
Ian Mahdieh
Nan Tenney
Ivan Hua
Patrick Petrychenko
Dylan Kane
Rishub Scand-inaro
Jonathan Jain
Romina Uesato
Datta Adam
Oskar Sadovsky
Dominik Bunyan
Rabiej Shimu
,
2024-11-01
Findings of the Association for Computational Linguistics: EMNLP 2024 (publié)
doi.org