Publications

GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
Nizar Islah
Justine Gehring
Diganta Misra
Eilif Benjamin Muller
Irina Rish
Terry Yue Zhuo
Massimo Caccia
2024-10-31
arXiv (published)
doi.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
2024-10-31
arXiv (published)
doi.org
arxiv.org
Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study
Doriane Olewicki
Leuson Da Silva
Suhaib Mujahid
Arezou Amini
Benjamin Mah
Marco Castelluccio
Sarra Habchi
Foutse Khomh
Bram Adams
2024-10-31
arXiv (published)
doi.org
arxiv.org
Inhibition of the inferior parietal lobe triggers state-dependent network adaptations
Kathleen A. Williams
Ole Numssen
Juan David Guerra
Jakub Kopal
Danilo Bzdok
Gesa Hartwigsen
The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in dai… (see more)ly life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.
2024-10-31
Heliyon (published)
doi.org
PP259 Topic: AS25–Sedation/Analgesia/Delirium/Withdrawal Syndrome/Other: PSYCHOMOTOR AGITATION DETECTION USING DEEP LEARNING
S. Dastani
A. Harakeh
A. Wiedemann
Philippe Jouvet
S. Ebrahimi Kahou
2024-10-31
Pediatric Critical Care Medicine (published)
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 (published)
doi.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… (see more) 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.
2024-10-31
arXiv (published)
doi.org
arxiv.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 (published)
doi.org
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 … (see more)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.
2024-10-31
arXiv (published)
doi.org
arxiv.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… (see more)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 (published)
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… (see more)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 (published)
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