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of 20 062
pro vyhledávání: '"P, Caron"'
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems
In this paper, we address the critical need for interpretable and uncertainty-aware machine learning models in the context of online learning for high-risk industries, particularly cyber-security. While deep learning and other complex models have dem
Externí odkaz:
http://arxiv.org/abs/2411.09393
Web-scale visual entity recognition, the task of associating images with their corresponding entities within vast knowledge bases like Wikipedia, presents significant challenges due to the lack of clean, large-scale training data. In this paper, we p
Externí odkaz:
http://arxiv.org/abs/2410.23676
Autor:
Cortinovis, Stefano, Caron, François
The Frequentist, Assisted by Bayes (FAB) framework aims to construct confidence regions that leverage information about parameter values in the form of a prior distribution. FAB confidence regions (FAB-CRs) have smaller volume for values of the param
Externí odkaz:
http://arxiv.org/abs/2410.20169
A significant challenge for autonomous cyber defence is ensuring a defensive agent's ability to generalise across diverse network topologies and configurations. This capability is necessary for agents to remain effective when deployed in dynamically
Externí odkaz:
http://arxiv.org/abs/2410.17647
Contact estimation is a key ability for limbed robots, where making and breaking contacts has a direct impact on state estimation and balance control. Existing approaches typically rely on gate-cycle priors or designated contact sensors. We design a
Externí odkaz:
http://arxiv.org/abs/2410.12345
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation
Autor:
Gauthier-Caron, Thomas, Siriwardhana, Shamane, Stein, Elliot, Ehghaghi, Malikeh, Goddard, Charles, McQuade, Mark, Solawetz, Jacob, Labonne, Maxime
By merging models, AI systems can combine the distinct strengths of separate language models, achieving a balance between multiple capabilities without requiring substantial retraining. However, the integration process can be intricate due to differe
Externí odkaz:
http://arxiv.org/abs/2410.08371
Overseas military personnel often face significant challenges in participating in elections due to the slow pace of traditional mail systems, which can result in ballots missing crucial deadlines. While internet-based voting offers a faster alternati
Externí odkaz:
http://arxiv.org/abs/2410.06705
Autor:
Arodi, Akshatha, Luck, Margaux, Bedwani, Jean-Luc, Zaimi, Aldo, Li, Ge, Pouliot, Nicolas, Beaudry, Julien, Caron, Gaétan Marceau
Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual
Externí odkaz:
http://arxiv.org/abs/2409.20353
With the advent of billion-parameter foundation models, efficient fine-tuning has become increasingly important for the adaptation of models to downstream tasks. However, especially in computer vision, it can be hard to achieve good performance when
Externí odkaz:
http://arxiv.org/abs/2409.07577
Data-driven methods demonstrate considerable potential for accelerating the inherently expensive computational fluid dynamics (CFD) solvers. Nevertheless, pure machine-learning surrogate models face challenges in ensuring physical consistency and sca
Externí odkaz:
http://arxiv.org/abs/2409.07175