Publications

Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing

Dmytro Humeniuk

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.

2024-01-01

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

2024-01-01

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

2024-01-01

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

2024-01-01

ENLSP (publié)

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.

2024-01-01

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.

2024-01-01

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

2024-01-01

AMAI@MICCAI (publié)

doi.org

arxiv.org

Scope Ambiguities in Large Language Models

Gaurav Kamath

Sebastian Schuster

Sowmya Vajjala

Siva Reddy

2024-01-01

Trans. Assoc. Comput. Linguistics (publié)

doi.org

arxiv.org

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation.

Guillaume Huguet

James Vuckovic

Kilian FATRAS

Eric Laufer

Pablo Lemos

Riashat Islam

Cheng-Hao Liu

Jarrid Rector-Brooks

Tara Akhound-Sadegh

Michael M. Bronstein

Alexander Tong

Joey Bose

2024-01-01

Neural Information Processing Systems (publié)

dblp.uni-trier.de

Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators

Sébastien Henwood

Goncalo Mordido

Yvon Savaria

Sarath 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.

2024-01-01

IEEECONF (publié)

doi.org

SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects

David Ifeoluwa Adelani

Hannah Liu

Xiaoyu Shen

Nikita Vassilyev

Jesujoba Oluwadara Alabi

Yanke Mao

Haonan Gao

Annie En-Shiun Lee