Publications

Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge.
Mathieu Boudreau
Agah Karakuzu
Ecem Bozkurt
Madeline Carr
Marco Castellaro
Luis Concha
Mariya Doneva
Seraina A. Dual
Alex Ensworth
Alexandru Foias
Véronique Fortier
Refaat E. Gabr
Guillaume Gilbert
Carri K. Glide‐Hurst
Matthew Grech‐Sollars
Siyuan Hu
Oscar Jalnefjord
Jorge Jovicich
Kübra Keskin … (voir 22 de plus)
Peter Koken
Anastasia Kolokotronis
Simran Kukran
Nam G. Lee
Ives R. Levesque
Bochao Li
Dan Ma
Burkhard Mädler
Nyasha G. Maforo
Jamie Near
Erick Pasaye
Alonso Ramirez‐Manzanares
Ben Statton
Christian Stehning
Stefano Tambalo
Ye Tian
Chenyang Wang
Kilian Weiss
Niloufar Zakariaei
Shuo Zhang
Ziwei Zhao
Nikola Stikov
PURPOSE T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, … (voir plus)and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. METHODS The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. RESULTS Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. CONCLUSION The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
David Dalrymple
David
Joar Max Viktor Skalse
Stuart Russell
Max Tegmark
Sanjit A. Seshia
Steve Omohundro
Christian Szegedy
Ben Goldhaber
Nora Ammann
Alessandro Abate
Joe Halpern
Clark Barrett
Ding Zhao
Zhi-Xuan Tan
Jeannette Wing
Joshua B. Tenenbaum
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with … (voir plus)a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.
Interpretability Needs a New Paradigm
Andreas Madsen
Himabindu Lakkaraju
Quantifying neurodegeneration of the cervical cord and brain in degenerative cervical myelopathy: A multicentre study using quantitative <scp>magnetic resonance imaging</scp>
Patrick Freund
Viveka Boller
Tim M. Emmenegger
Muhammad Akbar
Markus Hupp
Nikolai Pfender
Claudia A. M. Gandini Wheeler-Kingshott
Michael G. Fehlings
Armin Curt
Maryam Seif
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Jonathan Wilder Lavington
Ke Zhang
Vasileios Lioutas
Matthew Niedoba
Yunpeng Liu
Dylan Green
Saeid Naderiparizi
Xiaoxuan Liang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Deep Clustering with Self-Supervision using Pairwise Similarities
Mohammadreza Sadeghi
Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper, we propo… (voir plus)se a novel deep clustering framework with self-supervision using pairwise similarities (DCSS). The proposed method consists of two successive phases. In the first phase, we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder that is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoder's latent space. In the second phase, we propose to employ pairwise similarities to create a
Characterizing the voxel-based approaches in radioembolization dosimetry with reDoseMC.
Taehyung Peter Kim
BACKGROUND Yttrium-90 ( 90 Y …
Sub-goal Distillation: A Method to Improve Small Language Agents
Maryam Hashemzadeh
Elias Stengel-Eskin
Marc-Alexandre Côté
While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational req… (voir plus)uirements and restricted number of calls constrain their practical utility, especially in long-horizon interactive tasks such as decision-making or in scenarios involving continuous ongoing tasks. To address these constraints, we propose a method for transferring the performance of an LLM with billions of parameters to a much smaller language model (770M parameters). Our approach involves constructing a hierarchical agent comprising a planning module, which learns through Knowledge Distillation from an LLM to generate sub-goals, and an execution module, which learns to accomplish these sub-goals using elementary actions. In detail, we leverage an LLM to annotate an oracle path with a sequence of sub-goals towards completing a goal. Subsequently, we utilize this annotated data to fine-tune both the planning and execution modules. Importantly, neither module relies on real-time access to an LLM during inference, significantly reducing the overall cost associated with LLM interactions to a fixed cost. In ScienceWorld, a challenging and multi-task interactive text environment, our method surpasses standard imitation learning based solely on elementary actions by 16.7% (absolute). Our analysis highlights the efficiency of our approach compared to other LLM-based methods. Our code and annotated data for distillation can be found on GitHub.
Hierarchies define the scalability of robot swarms
Vivek Shankar Vardharajan
Karthik Soma
Sepand Dyanatkar
Pierre-Yves Lajoie
The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarm… (voir plus)s are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature's preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms.
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh J. Jain
Almer M. van der Sloot
Eric Jolicoeur
Edward Ruediger
Andrei Cristian Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor I Butoi
Jarrid Rector-Brooks
Simon R. Blackburn
Leo Feng
Hadi Nekoei
Sai Krishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampášek … (voir 14 de plus)
Sasikanth Avancha
William L. Hamilton
Brooks Paige
Sanchit Misra
Stanisław Jastrzębski
Bharat Kaul
José Miguel Hernández-Lobato
Marwin Segler
Michael M. Bronstein
Anne Marinier
Mike Tyers
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
Schrödinger's Update: User Perceptions of Uncertainties in Proprietary Large Language Model Updates
Zilin Ma
Yiyang Mei
Krzysztof Z. Gajos
2851: Operational Ontology for Oncology (O3) - Multi-professional society standard supporting AI
Charles S. Mayo
Mary U. Feng
Kristy K. Brock
Randi Kudner
Peter Balter
Jeffrey Buchsbaum
Amanda Caissie
Emily Daugherty
Andre Dekker
Clifton D. Fuller
Julian Hong
David Hong
Sophia Kamran
Evangelia Katsoulakis
Andra Krauze
Jon Kruse
Todd McNutt
Michelle Mierzwa
Amy Moreno … (voir 5 de plus)
Jatinder Palta
Richard Popple
Thomas Purdie
Susan Yom
Xiao Ying