Zobrazeno 1 - 10
of 75
pro vyhledávání: '"MONIZ, NUNO"'
Sharing private data for learning tasks is pivotal for transparent and secure machine learning applications. Many privacy-preserving techniques have been proposed for this task aiming to transform the data while ensuring the privacy of individuals. S
Externí odkaz:
http://arxiv.org/abs/2406.16456
Multiple synthetic data generation models have emerged, among which deep learning models have become the vanguard due to their ability to capture the underlying characteristics of the original data. However, the resemblance of the synthetic to the or
Externí odkaz:
http://arxiv.org/abs/2406.02736
Autor:
Kim, Dongwhi, Moniz, Nuno
As machine learning continues to gain prominence, transparency and explainability are increasingly critical. Without an understanding of these models, they can replicate and worsen human bias, adversely affecting marginalized communities. Algorithmic
Externí odkaz:
http://arxiv.org/abs/2405.19072
A long-standing dilemma prevents the broader application of explanation methods: general applicability and inference speed. On the one hand, existing model-agnostic explanation methods usually make minimal pre-assumptions about the prediction models
Externí odkaz:
http://arxiv.org/abs/2405.18664
Many evaluation metrics can be used to assess the performance of models in binary classification tasks. However, most of them are derived from a confusion matrix in a non-differentiable form, making it very difficult to generate a differentiable loss
Externí odkaz:
http://arxiv.org/abs/2405.14745
Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they
Externí odkaz:
http://arxiv.org/abs/2404.18537
Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed, particularly when it comes to the role of uncertainty. Selective regre
Externí odkaz:
http://arxiv.org/abs/2402.16300
Autor:
Ma, Yihong, Huang, Xiaobao, Nan, Bozhao, Moniz, Nuno, Zhang, Xiangliang, Wiest, Olaf, Chawla, Nitesh V.
The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction. Accurate yield prediction can guide chemists toward selecting high-yield reactions during synt
Externí odkaz:
http://arxiv.org/abs/2402.05971
In the context of the global seafood industry, the Azores archipelago (Portugal) plays a pivotal role due to its vast maritime domain. This study employs complex network analysis techniques to investigate the dynamics of Azores fisheries, using time
Externí odkaz:
http://arxiv.org/abs/2309.09378
Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing gear, or metier, is a key factor significantly impacting marine habit
Externí odkaz:
http://arxiv.org/abs/2309.09326