Publications

Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots
Simon Chamorro
Victor Klemm
Miguel de La Iglesia Valls
Christopher Pal
Roland Siegwart
2023-12-31
ICRA (publié)
doi.org
arxiv.org
Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing
Dmytro Humeniuk
Foutse Khomh
Giuliano Antoniol
Evolutionary search-based techniques are commonly used for testing autonomous robotic systems. However, these approaches often rely on compu… (voir plus)tationally expensive simulator-based models for test scenario evaluation. To improve the computational efficiency of the search-based testing, we propose augmenting the evolutionary search (ES) with a reinforcement learning (RL) agent trained using surrogate rewards derived from domain knowledge. In our approach, known as RIGAA (Reinforcement learning Informed Genetic Algorithm for Autonomous systems testing), we first train an RL agent to learn useful constraints of the problem and then use it to produce a certain part of the initial population of the search algorithm. By incorporating an RL agent into the search process, we aim to guide the algorithm towards promising regions of the search space from the start, enabling more efficient exploration of the solution space. We evaluate RIGAA on two case studies: maze generation for an autonomous ant robot and road topology generation for an autonomous vehicle lane keeping assist system. In both case studies, RIGAA converges faster to fitter solutions and produces a better test suite (in terms of average test scenario fitness and diversity). RIGAA also outperforms the state-of-the-art tools for vehicle lane keeping assist system testing, such as AmbieGen and Frenetic.
2023-12-31
ACM Trans. Softw. Eng. Methodol. (publié)
doi.org
arxiv.org
Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox
Jan Valosek
Julien Cohen-Adad
The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing cr… (voir plus)itical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).
2023-12-31
Magnetic Resonance in Medical Sciences (publié)
doi.org
Resilience and Mental-Health Symptoms in ICU Healthcare Professionals Facing Repeated COVID-19 Waves
Elie Azoulay
Frédéric Pochard
Laurent Argaud
Alain Cariou
Raphael Clere-Jehl
Olivier Guisset
Vincent Labbé
Fabienne Tamion
Fabrice Bruneel
Mercé Jourdain
Danielle Reuter
Kada Klouche
Achille Kouatchet
Virginie Souppart
Alexandre Lautrette
Julien Bohé
Antoine Vieillard Baron
Jean Dellamonica
Laurent Papazian
Jean Reignier … (voir 3 de plus)
François Barbier
Guillaume Dumas
Nancy Kentish-Barnes
2023-12-31
American Journal of Respiratory and Critical Care Medicine (publié)
doi.org
RGP: Achieving Memory-Efficient Model Fine-tuning Via Randomized Gradient Projection
Ali Saheb Pasand
Pouya Bashivan
2023-12-31
ENLSP (publié)
proceedings.mlr.press
proceedings.mlr.press
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning
Bac Nguyen
Stefan Uhlich
Fabien Cardinaux
Lukas Mauch
Marzieh Edraki
Aaron Courville
Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the fie… (voir plus)ld of machine learning. While a pre-trained vision-language model like CLIP has demonstrated remarkable zero-shot performance, further adaptation of the model to downstream tasks leads to undesirable degradation for OOD data. In this work, we introduce Sparse Adaptation for Fine-Tuning (SAFT), a method that prevents fine-tuning from forgetting the general knowledge in the pre-trained model. SAFT only updates a small subset of important parameters whose gradient magnitude is large, while keeping the other parameters frozen. SAFT is straightforward to implement and conceptually simple. Extensive experiments show that with only 0.1% of the model parameters, SAFT can significantly improve the performance of CLIP. It consistently outperforms baseline methods across several benchmarks. On the few-shot learning benchmark of ImageNet and its variants, SAFT gives a gain of 5.15% on average over the conventional fine-tuning method in OOD settings.
2023-12-31
ECCV (69) (publié)
doi.org
arxiv.org
Scaling Laws Do Not Scale
Fernando Diaz
Michael Madaio
Recent work has proposed a power law relationship, referred to as ``scaling laws,'' between the performance of artificial intelligence (AI) … (voir plus)models and aspects of those models' design (e.g., dataset size). In other words, as the size of a dataset (or model parameters, etc) increases, the performance of a given model trained on that dataset will correspondingly increase. However, while compelling in the aggregate, this scaling law relationship overlooks the ways that metrics used to measure performance may be precarious and contested, or may not correspond with how different groups of people may perceive the quality of models' output. In this paper, we argue that as the size of datasets used to train large AI models grows, the number of distinct communities (including demographic groups) whose data is included in a given dataset is likely to grow, each of whom may have different values. As a result, there is an increased risk that communities represented in a dataset may have values or preferences not captured by (or in the worst case, at odds with) the metrics used to evaluate model performance for scaling laws. We end the paper with implications for AI scaling laws -- that models may not, in fact, continue to improve as the datasets get larger -- at least not for all people or communities impacted by those models.
2023-12-31
AIES (1) (publié)
doi.org
arxiv.org
SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury
Enamundram Naga Karthik
Jan Valosek
Lynn Farner
Dario Pfyffer
Simon Schading-Sassenhausen
Anna Lebret
Gergely David
Andrew C. Smith
Kenneth A. Weber
Maryam Seif
Rhscir Network Imaging Group
Patrick Freund
Julien Cohen-Adad
2023-12-31
AMAI@MICCAI (publié)
doi.org
arxiv.org
Scope Ambiguities in Large Language Models
Gaurav Kamath
Sebastian Schuster
Sowmya Vajjala
Siva Reddy
Sentences containing multiple semantic operators with overlapping scope often create ambiguities in interpretation, known as scope ambiguiti… (voir plus)es. These ambiguities offer rich insights into the interaction between semantic structure and world knowledge in language processing. Despite this, there has been little research into how modern large language models treat them. In this paper, we investigate how different versions of certain autoregressive language models -- GPT-2, GPT-3/3.5, Llama 2 and GPT-4 -- treat scope ambiguous sentences, and compare this with human judgments. We introduce novel datasets that contain a joint total of almost 1,000 unique scope-ambiguous sentences, containing interactions between a range of semantic operators, and annotated for human judgments. Using these datasets, we find evidence that several models (i) are sensitive to the meaning ambiguity in these sentences, in a way that patterns well with human judgments, and (ii) can successfully identify human-preferred readings at a high level of accuracy (over 90% in some cases).
2023-12-31
Trans. Assoc. Comput. Linguistics (publié)
doi.org
arxiv.org
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links
Alex Fedorov
Eloy Geenjaar
Lei Wu
Tristan Sylvain
Thomas P. DeRamus
Margaux Luck
Maria Misiura
Girish Mittapalle
R. Devon Hjelm
Sergey M. Plis
Vince D. Calhoun
In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, … (voir plus)conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.
2023-12-31
NeuroImage (publié)
doi.org
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Guillaume Huguet
James Vuckovic
Kilian Fatras
Éric Thibodeau-Laufer
Pablo Lemos
Riashat Islam
Cheng-Hao Liu
Jarrid Rector-Brooks
Tara Akhound-Sadegh
Michael Bronstein
Alexander Tong
Avishek Joey Bose
2023-12-31
Advances in Neural Information Processing Systems 37 (publié)
doi.org
openreview.net
Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators
Sébastien Henwood
Goncalo Mordido
Yvon Savaria
A. Chandar
François Leduc-Primeau
Many deep neural network (DNN) models consume a significant amount of energy at inference time, in large part due to energy consumed by memo… (voir plus)ry access. In-memory computing addresses this problem by eliminating many memory accesses, but exposes model weights to noise and circuit variations. While several methods have been proposed to train DNNs robust to weight noise they typically require knowledge of the noise distribution, or degrade the DNN performance in noiseless setting. In this work, we first show that applying sharpness-aware training, by optimizing for both the loss value and loss sharpness, significantly improves robustness to noisy weights at inference time. Then, we propose a new adaptive sharpness-aware method that conditions the worst-case perturbation of a given weight not only on its magnitude but also on the range of the weight distribution. This is achieved by performing sharpness-aware minimization scaled by outlier normalization (SAMSON). Results on computer-vision benchmarks show that SAMSON increases model robustness to noisy weights without compromising generalization performance in noiseless regimes.
2023-12-31
IEEECONF (publié)
doi.org