Publications

OpenQDC: Open Quantum Data Commons
Cristian Gabellini
Nikhil Shenoy
Stephan Thaler
Semih Cantürk
Daniel McNeela
Dominique Beaini
Michael Bronstein
Prudencio Tossou
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, … (voir plus)offering precise and rapid energy and force calculations. However, Quantum-Mechanical (QM) datasets, crucial for MLIPs, are fragmented across various repositories, hindering accessibility and model development. We introduce the openQDC package, consolidating 37 QM datasets from over 250 quantum methods and 400 million geometries into a single, accessible resource. These datasets are meticulously preprocessed, and standardized for MLIP training, covering a wide range of chemical elements and interactions relevant in organic chemistry. OpenQDC includes tools for normalization and integration, easily accessible via Python. Experiments with well-known architectures like SchNet, TorchMD-Net, and DimeNet reveal challenges for those architectures and constitute a leaderboard to accelerate benchmarking and guide novel algorithms development. Continuously adding datasets to OpenQDC will democratize QM dataset access, foster more collaboration and innovation, enhance MLIP development, and support their adoption in the MD field.
2024-10-31
arXiv (publié)
doi.org
Predictive Modeling of Body Image Dissatisfaction in People With Type 1 Diabetes
COURTNEY SOUTH
SHAHRYAR EBRAHIMI
ANNE-SOPHIE BRAZEAU
Maria Cutumisu
2024-10-31
Canadian Journal of Diabetes (publié)
doi.org
SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals
Davide Borra
Francesco Paissan
Mirco Ravanelli
2024-10-31
Computers in Biology and Medicine (publié)
doi.org
Improving Adversarial Transferability via Model Alignment
Avery Ma
Amir-massoud Farahmand
Yangchen Pan
Philip Torr
Jindong Gu
Neural networks are susceptible to adversarial perturbations that are transferable across different models. In this paper, we introduce a no… (voir plus)vel model alignment technique aimed at improving a given source model's ability in generating transferable adversarial perturbations. During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss. This loss measures the divergence in the predictions between the source model and another, independently trained model, referred to as the witness model. To understand the effect of model alignment, we conduct a geometric analysis of the resulting changes in the loss landscape. Extensive experiments on the ImageNet dataset, using a variety of model architectures, demonstrate that perturbations generated from aligned source models exhibit significantly higher transferability than those from the original source model.
2024-10-30
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Soft Condorcet Optimization for Ranking of General Agents
Marc Lanctot
Kate Larson
Michael Kaisers
Quentin Berthet
Ian Gemp
Manfred Diaz
Roberto-Rafael Maura-Rivero
Yoram Bachrach
Anna Koop
Doina Precup
A common way to drive progress of AI models and agents is to compare their performance on standardized benchmarks. Comparing the performance… (voir plus) of general agents requires aggregating their individual performances across a potentially wide variety of different tasks. In this paper, we describe a novel ranking scheme inspired by social choice frameworks, called Soft Condorcet Optimization (SCO), to compute the optimal ranking of agents: the one that makes the fewest mistakes in predicting the agent comparisons in the evaluation data. This optimal ranking is the maximum likelihood estimate when evaluation data (which we view as votes) are interpreted as noisy samples from a ground truth ranking, a solution to Condorcet's original voting system criteria. SCO ratings are maximal for Condorcet winners when they exist, which we show is not necessarily true for the classical rating system Elo. We propose three optimization algorithms to compute SCO ratings and evaluate their empirical performance. When serving as an approximation to the Kemeny-Young voting method, SCO rankings are on average 0 to 0.043 away from the optimal ranking in normalized Kendall-tau distance across 865 preference profiles from the PrefLib open ranking archive. In a simulated noisy tournament setting, SCO achieves accurate approximations to the ground truth ranking and the best among several baselines when 59\% or more of the preference data is missing. Finally, SCO ranking provides the best approximation to the optimal ranking, measured on held-out test sets, in a problem containing 52,958 human players across 31,049 games of the classic seven-player game of Diplomacy.
2024-10-30
ArXiv (prépublication)
doi.org
arxiv.org
Unclocklike biological oscillators with frequency memory
Christian Mauffette Denis
Paul François
Entrainment experiments on the vertebrate segmentation clock have revealed that embryonic oscillators actively change their internal frequen… (voir plus)cy to adapt to the driving signal. This is not consistent with either a one-dimensional clock model or a limit-cycle model, but rather it suggests a new “unclocklike” behavior. In this work, we propose simple, biologically realistic descriptions of such internal frequency adaptation, where a phase oscillator activates a memory variable controlling the oscillator's frequency. We study two opposite limits for the control of the memory variable, one with a smooth phase-averaging memory field, and the other with a pulsatile, phase-dependent activation. Both models recapitulate intriguing properties of the entrained segmentation clock, such as very broad Arnold tongues and an entrainment phase plateauing with detuning. We compute analytically multiple properties of such systems, such as entrainment phases and cycle shapes. We further describe new phenomena, including hysteresis in entrainment, bistability in the frequency of the entrained oscillator, and probabilistic entrainment. Our work shows that oscillators with frequency memory can exhibit new classes of unclocklike properties that can be tested through experimental entrainment. Published by the American Physical Society 2024
2024-10-30
Physical Review X Life (publié)
doi.org
arxiv.org
Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis
Zehao Jin
Mario Pasquato
Benjamin L. Davis
Andrea Maccio
Yashar Hezaveh
2024-10-29
NeurIPS.cc/2024/Workshop/CRL (poster)
openreview.net
openreview.net
General Causal Imputation via Synthetic Interventions
Marco Jiralerspong
Thomas Jiralerspong
Vedant Shah
Dhanya Sridhar
Gauthier Gidel
Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their inte… (voir plus)ractions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.
2024-10-29
NeurIPS.cc/2024/Workshop/CRL (poster)
doi.org
openreview.net
Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy
Jos'e-Fabian Villa-V'asquez
Marco Pedersoli
Unsupervised object discovery is commonly interpreted as the task of localizing and/or categorizing objects in visual data without the need … (voir plus)for labeled examples. While current object recognition methods have proven highly effective for practical applications, the ongoing demand for annotated data in real-world scenarios drives research into unsupervised approaches. Furthermore, existing literature in object discovery is both extensive and diverse, posing a significant challenge for researchers that aim to navigate and synthesize this knowledge. Motivated by the evidenced interest in this avenue of research, and the lack of comprehensive studies that could facilitate a holistic understanding of unsupervised object discovery, this survey conducts an in-depth exploration of the existing approaches and systematically categorizes this compendium based on the tasks addressed and the families of techniques employed. Additionally, we present an overview of common datasets and metrics, highlighting the challenges of comparing methods due to varying evaluation protocols. This work intends to provide practitioners with an insightful perspective on the domain, with the hope of inspiring new ideas and fostering a deeper understanding of object discovery approaches.
2024-10-29
ArXiv (prépublication)
doi.org
arxiv.org
From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
Shalaleh Rismani
Roel Dobbe
AJung Moon
2024-10-28
ArXiv (prépublication)
doi.org
arxiv.org
Injury and violence in the context of sustainable development
Ronald Lett
Tsegazeab Laeke
Respicious Boniface
Elise Presser
Berjo Dongmo Takoutsing
Emnet Tesfay Shimber
Adam Gyedu
Atalel Awedew
Halid Melkamu
Riya Sawhney
Mohammed A.S Abdullahi
Alemayehu Bedada
Shemsedin Ibro
Meseret Abeza
Matthew Boroditsky
Roy Hilzenrat
Felix Oyania
Dionysia Kravarioti
Maryse Bouchard
Riya Sawhney … (voir 80 de plus)
Gilgamesh Eamer
Chibuike Onu
Eunice Onuh
Daniel Kisitu Kyengera
Netsanet Abebe
Dagim Berhanu
Riya Sawhney
Shreenik Kundu
Mengistu Ayele
Shemsedin Amme Ibro
Gabriel Rodriguez
Samuel Girma
Hussien Endris Assen
Ayenew Gucho
Brian Kasagga
Degisew Dersso Mengistu
Davy Lau
Francesca Vituci
Tsion Seyoum
Ifeanyichukwu Onah
Dagim Berhanu
Wongel Tena Shale
Kidist Bartolomeos
Ryan Lett
Abenezer Tirsit
Azarias Kassahun
Victoria Munthali
Tarek Razek
Dan Deckelbaum
David Bracco
Ermiyas Belay
Fitsum Kifle
Thomas G. Weiser
Katie Iverson
David Ulrich Dalle
Celestin Bilong Mbangtang
Arsene Daniel Nyalundja
Jondre Macaraeg
Irene Dzirasa
Ulrick Sidney Kanmounye
Delanyo Dovlo
Kwadwo Koram
Eugene Nyarko
Desmond T. Jumbam
Taylor Jaraczewski
Maria Sgro
Ajiel Mae Basmayor
Asegid Ergete
Katherine Iverson
Mary Schroeder
Christopher Dodgion
Emmanuel Nakua
Peter Donkor
Charles Mock
Tsegazeab Laeke
Mestet Yibeltal
Mengistu Ayele
Sisay Bekele
Berhanu Hailemariam
Enku Shiferaw
Yishak Shiferaw
Wubetie Yirdaw
Shlok Patel
Debojit Basak
Deepa Kizhakke Veetil
Nobhojit Roy
Martin Gerdin Wärnberg
Santosh Rath
Kefas Mbaya
Abubakar Kakasanda
Stephanie Danjuma
Hector Olasoji
Emmanuel Ameh
Mpapho Joseph Motsumi
Shimelis Genna Hamda
Demuma Amdisa
Getachew Tilahun
Tsegazeab Laeke
Mark Hill
Rachel Livergant
invaluable
2024-10-28
Canadian journal of surgery. Journal canadien de chirurgie (publié)
doi.org
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
Julien Pallage
Bertrand Scherrer
Salma Naccache
Christophe B'elanger
Antoine Lesage-Landry
2024-10-28
ArXiv (prépublication)
doi.org
arxiv.org