Zobrazeno 1 - 10
of 742
pro vyhledávání: '"Saeys Yvan"'
Autor:
Gevaert, Arne, Saeys, Yvan
The black box problem in machine learning has led to the introduction of an ever-increasing set of explanation methods for complex models. These explanations have different properties, which in turn has led to the problem of method selection: which e
Externí odkaz:
http://arxiv.org/abs/2412.13623
Autor:
Heiter, Edith, Martens, Liesbet, Seurinck, Ruth, Guilliams, Martin, De Bie, Tijl, Saeys, Yvan, Lijffijt, Jefrey
This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize preserving either local neighborhoods or global distances, but insights
Externí odkaz:
http://arxiv.org/abs/2406.12953
Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in
Externí odkaz:
http://arxiv.org/abs/2404.16358
Recent advances in dimensionality reduction have achieved more accurate lower-dimensional embeddings of high-dimensional data. In addition to visualisation purposes, these embeddings can be used for downstream processing, including batch effect norma
Externí odkaz:
http://arxiv.org/abs/2309.02917
Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topological knowledge about the data, such as a known cluster structure or t
Externí odkaz:
http://arxiv.org/abs/2301.03338
Deep Reinforcement Learning uses a deep neural network to encode a policy, which achieves very good performance in a wide range of applications but is widely regarded as a black box model. A more interpretable alternative to deep networks is given by
Externí odkaz:
http://arxiv.org/abs/2209.03357
Autor:
Gevaert, Arne, Saeys, Yvan
Because of their strong theoretical properties, Shapley values have become very popular as a way to explain predictions made by black box models. Unfortuately, most existing techniques to compute Shapley values are computationally very expensive. We
Externí odkaz:
http://arxiv.org/abs/2208.12595
Autor:
Gevaert, Arne, Rousseau, Axel-Jan, Becker, Thijs, Valkenborg, Dirk, De Bie, Tijl, Saeys, Yvan
Feature attribution maps are a popular approach to highlight the most important pixels in an image for a given prediction of a model. Despite a recent growth in popularity and available methods, little attention is given to the objective evaluation o
Externí odkaz:
http://arxiv.org/abs/2202.12270
Autor:
Daelemans Walter, Van de Peer Yves, Van Asch Vincent, Morante Roser, Van Landeghem Sofie, Abeel Thomas, Saeys Yvan
Publikováno v:
BMC Bioinformatics, Vol 11, Iss Suppl 5, p I1 (2010)
Externí odkaz:
https://doaj.org/article/e080648e94184599aed0132bb19607d4
Unsupervised feature learning often finds low-dimensional embeddings that capture the structure of complex data. For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to highe
Externí odkaz:
http://arxiv.org/abs/2110.09193