Zobrazeno 1 - 10
of 87
pro vyhledávání: '"van Rijn, Jan N."'
Autor:
Gomes, Inês, Teixeira, Luís F., van Rijn, Jan N., Soares, Carlos, Restivo, André, Cunha, Luís, Santos, Moisés
The increasing use of deep learning across various domains highlights the importance of understanding the decision-making processes of these black-box models. Recent research focusing on the decision boundaries of deep classifiers, relies on generate
Externí odkaz:
http://arxiv.org/abs/2408.06302
The ubiquity of deep learning algorithms in various applications has amplified the need for assuring their robustness against small input perturbations such as those occurring in adversarial attacks. Existing complete verification techniques offer pr
Externí odkaz:
http://arxiv.org/abs/2406.10154
Autor:
Egele, Romain, Junior, Julio C. S. Jacques, van Rijn, Jan N., Guyon, Isabelle, Baró, Xavier, Clapés, Albert, Balaprakash, Prasanna, Escalera, Sergio, Moeslund, Thomas, Wan, Jun
Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's
Externí odkaz:
http://arxiv.org/abs/2404.09703
Deep learning requires large amounts of data to learn new tasks well, limiting its applicability to domains where such data is available. Meta-learning overcomes this limitation by learning how to learn. In 2001, Hochreiter et al. showed that an LSTM
Externí odkaz:
http://arxiv.org/abs/2310.14139
Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent. While these methods have achieved successes in various scenarios, th
Externí odkaz:
http://arxiv.org/abs/2310.09028
Deep neural networks can yield good performance on various tasks but often require large amounts of data to train them. Meta-learning received considerable attention as one approach to improve the generalization of these networks from a limited amoun
Externí odkaz:
http://arxiv.org/abs/2310.06148
Autor:
Tuia, Devis, Schindler, Konrad, Demir, Begüm, Zhu, Xiao Xiang, Kochupillai, Mrinalini, Džeroski, Sašo, van Rijn, Jan N., Hoos, Holger H., Del Frate, Fabio, Datcu, Mihai, Markl, Volker, Saux, Bertrand Le, Schneider, Rochelle, Camps-Valls, Gustau
Publikováno v:
IEEE Geoscience and Remote Sensing Magazine, 2024
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's eye view of the essential scientific tools and approaches informing a
Externí odkaz:
http://arxiv.org/abs/2305.08413
Autor:
Ullah, Ihsan, Carrión-Ojeda, Dustin, Escalera, Sergio, Guyon, Isabelle, Huisman, Mike, Mohr, Felix, van Rijn, Jan N, Sun, Haozhe, Vanschoren, Joaquin, Vu, Phan Anh
Publikováno v:
36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks., NeurIPS, Nov 2022, New Orleans, United States
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open datasets, each having at least 20 classes with 40 examples per class, wit
Externí odkaz:
http://arxiv.org/abs/2302.08909
This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usuall
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/53319
As restricted quantum computers are slowly becoming a reality, the search for meaningful first applications intensifies. In this domain, one of the more investigated approaches is the use of a special type of quantum circuit - a so-called quantum neu
Externí odkaz:
http://arxiv.org/abs/2206.09992