Publications

PEACE: Prompt Engineering Automation for CLIPSeg Enhancement for Safe-Landing Zone Segmentation
Haechan Mark Bong
Rongge Zhang
Antoine Robillard
Giovanni Beltrame
Safe landing is essential in robotics applications, from industrial settings to space exploration. As artificial intelligence advances, we h… (voir plus)ave developed PEACE (Prompt Engineering Automation for CLIPSeg Enhancement), a system that automatically generates and refines prompts for identifying landing zones in changing environments. Traditional approaches using fixed prompts for open-vocabulary models struggle with environmental changes and can lead to dangerous outcomes when conditions are not represented in the predefined prompts. PEACE addresses this limitation by dynamically adapting to shifting data distributions. Our key innovation is the dual segmentation of safe and unsafe landing zones, allowing the system to refine the results by removing unsafe areas from potential landing sites. Using only monocular cameras and image segmentation, PEACE can safely guide descent operations from 100 meters to altitudes as low as 20 meters. The testing shows that PEACE significantly outperforms the standard CLIP and CLIPSeg prompting methods, improving the successful identification of safe landing zones from 57% to 92%. We have also demonstrated enhanced performance when replacing CLIPSeg with FastSAM. The complete source code is available as an open-source software 1.
2024-12-31
IROS (publié)
doi.org
arxiv.org
Performance modulations phase-locked to action depend on internal state
Tommaso Tosato
Guillaume Dumas
Gustavo Rohenkohl
Pascal Fries
Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulatio… (voir plus)ns of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participant’s internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at ≈17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a “miss” showed an action-related modulation at ≈17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at ≈17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.
2024-12-31
iScience (publié)
doi.org
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Antonios Valkanas
Yuening Wang
Yingxue Zhang
Mark J. Coates
2024-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
PETRA: Parallel End-to-End Training of Reversible Architectures
Stéphane Rivaud
Louis Fournier
Thomas Pumir
Eugene Belilovsky
Mickael Eickenberg
Edouard Oyallon
Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (voir plus)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.
2024-12-31
ICLR (publié)
doi.org
openreview.net
Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging
Zahra Tehrani Nasab
Amar Kumar
Tal Arbel
Advancements in diffusion-based foundation models have improved text-to-image generation, yet most efforts have been limited to low-resoluti… (voir plus)on settings. As high-resolution image synthesis becomes increasingly essential for various applications, particularly in medical imaging domains, fine-tuning emerges as a crucial mechanism for adapting these powerful pre-trained models to task-specific requirements and data distributions. In this work, we present a systematic study, examining the impact of various fine-tuning techniques on image generation quality when scaling to high resolution 512x512 pixels. We benchmark a diverse set of fine-tuning methods, including full fine-tuning strategies and parameter-efficient fine-tuning (PEFT). We dissect how different fine-tuning methods influence key quality metrics, including Fr\'echet Inception Distance (FID), Vendi score, and prompt-image alignment. We also evaluate the utility of generated images in a downstream classification task under data-scarce conditions, demonstrating that specific fine-tuning strategies improve both generation fidelity and downstream performance when synthetic images are used for classifier training and evaluation on real images. Our code is accessible through the project website - https://tehraninasab.github.io/PixelUPressure/.
2024-12-31
ELAMI@MICCAI (publié)
doi.org
arxiv.org
Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach
Meera Desai
A. Feder Cooper
Angelina Wang
Chad Atalla
Solon Barocas
Su Lin Blodgett
Alexandra Chouldechova
Emily Corvi
P. A. Dow
Jean Garcia-Gathright
A.R. Olteanu
Nicholas Pangakis
Stefanie Reed
Emily Sheng
Dan Vann
Jennifer Wortman Vaughan
Matthew Vogel
Hannah Washington
Abigail Z. Jacobs
The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as"a… (voir plus) tangle of sloppy tests [and] apples-to-oranges comparisons"(Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI. This framework has two important implications for designing and evaluating evaluations: First, it can broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates. Second, it brings rigor to both conceptual and operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.
2024-12-31
ICML (Position Papers) (publié)
doi.org
proceedings.mlr.press
Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations
Jean-Luc Lupien
Antoine Lesage-Landry
Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (voir plus)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.
2024-12-31
Electric Power Systems Research (publié)
doi.org
arxiv.org
Predicting Vessel Speed Over Ground: A Machine Learning Approach for Enhancing Maritime Transport
Ismail Bourzak
Loubna Benabbou
Sara El Mekkaoui
Abdelaziz Berrado
Stéphane Caron
2024-12-31
IFAC-PapersOnLine (publié)
doi.org
PreSumm: Predicting Summarization Performance Without Summarizing
Steven Koniaev
Ori Ernst
Jackie Chi Kit Cheung
2024-12-31
Findings of the Association for Computational Linguistics: ACL (publié)
doi.org
Proceedings of the 18th Workshop on Building and Using Comparable Corpora (BUCC)
Serge Sharoff
Ayla Rigouts Terryn
Pierre Zweigenbaum
Reinhard Rapp
2024-12-31
COLING Workshops (publié)
dblp.uni-trier.de
A protocol for trustworthy EEG decoding with neural networks
Davide Borra
Elisa Magosso
Mirco Ravanelli
2024-12-31
Neural Networks (publié)
doi.org
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
Brice Rauby
Paul Xing
Jonathan Porée
Maxime Gasse
Jean Provost
Ultrasound Localization Microscopy (ULM) is a non-invasive technique that allows for the imaging of micro-vessels in vivo, at depth and with… (voir plus) a resolution on the order of ten microns. ULM is based on the sub-resolution localization of individual microbubbles injected in the bloodstream. Mapping the whole angioarchitecture requires the accumulation of microbubbles trajectories from thousands of frames, typically acquired over a few minutes. ULM acquisition times can be reduced by increasing the microbubble concentration, but requires more advanced algorithms to detect them individually. Several deep learning approaches have been proposed for this task, but they remain limited to 2D imaging, in part due to the associated large memory requirements. Herein, we propose to use sparse tensor neural networks to reduce memory usage in 2D and to improve the scaling of the memory requirement for the extension of deep learning architecture to 3D. We study several approaches to efficiently convert ultrasound data into a sparse format and study the impact of the associated loss of information. When applied in 2D, the sparse formulation reduces the memory requirements by a factor 2 at the cost of a small reduction of performance when compared against dense networks. In 3D, the proposed approach reduces memory requirements by two order of magnitude while largely outperforming conventional ULM in high concentration settings. We show that Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense deep learning based method in 2D ULM i.e. the use of higher concentration in silico and reduced acquisition time.
2024-12-31
IEEE Transactions on Image Processing (inconnu)
doi.org
arxiv.org