Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Joachim Schreurs"'
Publikováno v:
SIAM Journal on Mathematics of Data Science. 4:1171-1190
Publikováno v:
SIAM Journal on Mathematics of Data Science
Kernel methods have achieved very good performance on large scale regression and classification problems, by using the Nystr\"om method and preconditioning techniques. The Nystr\"om approximation -- based on a subset of landmarks -- gives a low rank
Selecting diverse and important items, called landmarks, from a large set is a problem of interest in machine learning. As a specific example, in order to deal with large training sets, kernel methods often rely on low rank matrix Nystr\"om approxima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::310e5e55540c516e526d49c1c04e7d4d
https://hal.science/hal-03511384
https://hal.science/hal-03511384
Publikováno v:
The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science
The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Oct 2021, Grasmere, United Kingdom
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning, Optimization, and Data Science
Machine Learning, Optimization, and Data Science ISBN: 9783030954697
The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Oct 2021, Grasmere, United Kingdom
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning, Optimization, and Data Science
Machine Learning, Optimization, and Data Science ISBN: 9783030954697
International audience; Commonly, machine learning models minimize an empirical expectation. As a result, the trained models typically perform well for the majority of the data but the performance may deteriorate in less dense regions of the dataset.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dffb96e8cd51348e5429ea148a763ffc
https://hal.science/hal-03511336
https://hal.science/hal-03511336
Publikováno v:
Neural computation. 34(10)
Disentanglement is a useful property in representation learning, which increases the interpretability of generative models such as variational autoencoders (VAE), generative adversarial models, and their many variants. Typically in such models, an in
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. Research Track
Machine Learning and Knowledge Discovery in Databases. Research Track, 12976, Springer International Publishing, pp.52-66, 2021, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-86520-7_4⟩
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning and Knowledge Discovery in Databases. Research Track
Machine Learning and Knowledge Discovery in Databases. Research Track-European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II
Machine Learning and Knowledge Discovery in Databases. Research Track ISBN: 9783030865191
ECML/PKDD (2)
Machine Learning and Knowledge Discovery in Databases. Research Track, 12976, Springer International Publishing, pp.52-66, 2021, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-86520-7_4⟩
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning and Knowledge Discovery in Databases. Research Track
Machine Learning and Knowledge Discovery in Databases. Research Track-European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II
Machine Learning and Knowledge Discovery in Databases. Research Track ISBN: 9783030865191
ECML/PKDD (2)
Generative Adversarial Networks (GANs) are performant generative methods yielding high-quality samples. However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i.e. the generative models not being able t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd84e8d271b52a7fef3e3d59e1569cba
https://hal.science/hal-03511345
https://hal.science/hal-03511345
Publikováno v:
Artificial Intelligence and Machine Learning-31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers
Communications in Computer and Information Science
Communications in Computer and Information Science-Artificial Intelligence and Machine Learning
Communications in Computer and Information Science ISBN: 9783030651534
BNAIC/BENELEARN (Selected Papers)
Communications in Computer and Information Science
Communications in Computer and Information Science-Artificial Intelligence and Machine Learning
Communications in Computer and Information Science ISBN: 9783030651534
BNAIC/BENELEARN (Selected Papers)
Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search. In this paper, we discuss a greedy deterministic adaptation of k-DPP. Determini
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a97e8e7c69751b0fbdcc8f790d3c9a0a
Publikováno v:
Neural Networks
This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a
Publikováno v:
Machine Learning, Optimization, and Data Science-6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I
Machine Learning, Optimization, and Data Science ISBN: 9783030645823
LOD (1)
Machine Learning, Optimization, and Data Science ISBN: 9783030645823
LOD (1)
Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded in the representation of the model. This results in the generation of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e34842c4aa37da05ea92b640c26b09f
https://lirias.kuleuven.be/handle/123456789/656739
https://lirias.kuleuven.be/handle/123456789/656739
Publikováno v:
Artificial Intelligence and Machine Learning-31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers
Communications in Computer and Information Science
Communications in Computer and Information Science-Artificial Intelligence and Machine Learning
Communications in Computer and Information Science ISBN: 9783030651534
BNAIC/BENELEARN (Selected Papers)
Communications in Computer and Information Science
Communications in Computer and Information Science-Artificial Intelligence and Machine Learning
Communications in Computer and Information Science ISBN: 9783030651534
BNAIC/BENELEARN (Selected Papers)
Kernel PCA is a powerful feature extractor which recently has seen a reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs allow for a representation of kernel PCA in terms of hidden and visible units similar to Restricted Bol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff426c3ca2592e8fa9d12eb5cf231276
https://lirias.kuleuven.be/handle/123456789/642623
https://lirias.kuleuven.be/handle/123456789/642623