Zobrazeno 1 - 10
of 244
pro vyhledávání: '"Cassio, F."'
Computer vision methods that explicitly detect object parts and reason on them are a step towards inherently interpretable models. Existing approaches that perform part discovery driven by a fine-grained classification task make very restrictive assu
Externí odkaz:
http://arxiv.org/abs/2407.04538
Publikováno v:
ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Sep 2024, Vilnius, Lithuania
Semi-supervised domain adaptation methods leverage information from a source labelled domain with the goal of generalizing over a scarcely labelled target domain. While this setting already poses challenges due to potential distribution shifts betwee
Externí odkaz:
http://arxiv.org/abs/2406.14087
Timely up-to-date land use/land cover (LULC) maps play a pivotal role in supporting agricultural territory management, environmental monitoring and facilitating well-informed and sustainable decision-making. Typically, when creating a land cover (LC)
Externí odkaz:
http://arxiv.org/abs/2404.11114
Autor:
van der Klis, Robert, Alaniz, Stephan, Mancini, Massimiliano, Dantas, Cassio F., Ienco, Dino, Akata, Zeynep, Marcos, Diego
Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds. Encouraging a fine-grained classification model to first detect such parts and then using them to infer the class could help
Externí odkaz:
http://arxiv.org/abs/2309.03173
Models for fine-grained image classification tasks, where the difference between some classes can be extremely subtle and the number of samples per class tends to be low, are particularly prone to picking up background-related biases and demand robus
Externí odkaz:
http://arxiv.org/abs/2308.12127
Counterfactual explanations are an emerging tool to enhance interpretability of deep learning models. Given a sample, these methods seek to find and display to the user similar samples across the decision boundary. In this paper, we propose a generat
Externí odkaz:
http://arxiv.org/abs/2301.01520
Autor:
Moreau, Thomas, Massias, Mathurin, Gramfort, Alexandre, Ablin, Pierre, Bannier, Pierre-Antoine, Charlier, Benjamin, Dagréou, Mathieu, la Tour, Tom Dupré, Durif, Ghislain, Dantas, Cassio F., Klopfenstein, Quentin, Larsson, Johan, Lai, En, Lefort, Tanguy, Malézieux, Benoit, Moufad, Badr, Nguyen, Binh T., Rakotomamonjy, Alain, Ramzi, Zaccharie, Salmon, Joseph, Vaiter, Samuel
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: rese
Externí odkaz:
http://arxiv.org/abs/2206.13424
Non-negative and bounded-variable linear regression problems arise in a variety of applications in machine learning and signal processing. In this paper, we propose a technique to accelerate existing solvers for these problems by identifying saturate
Externí odkaz:
http://arxiv.org/abs/2202.07258
Publikováno v:
Journal of Machine Learning Research, 2021, 22 (236), pp.1-57
Sparse optimization problems are ubiquitous in many fields such as statistics, signal/image processing and machine learning. This has led to the birth of many iterative algorithms to solve them. A powerful strategy to boost the performance of these a
Externí odkaz:
http://arxiv.org/abs/2102.10846
Publikováno v:
In Environmental Pollution 15 March 2024 345
