Machine Learning and Knowledge Discovery in Databases, Springer, 18 janvier 2019
Parametric embedding methods such as parametric t-distributed Stochastic Neighbor Embedding (pt-SNE) enables out-of-sample data visualization without further...
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 12 septembre 2023, Volume : 45, Numéro : 12
We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem...
Research in Computational Molecular Biology: 27th Annual International Conference, Springer, 3 avril 2023
T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes...
Bioinformatics, Oxford University Press (OUP), 24 janvier 2023, Volume : 39, Numéro : 2
Motivation: MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of...
Journal of Intelligent Information Systems, Springer US, 1 avril 2013, Volume : 40, Numéro : 2
Many real-world applications require the simultaneous prediction of multiple target attributes. The techniques currently available for these problems either...
Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 16 juin 2020, Volume : 34, Numéro : 4
Data augmentation with Mixup (Zhang et al. 2018) has shown to be an effective model regularizer for current art deep classification networks. It generates...
We present a simple and yet effective interpolation-based regularization technique, aiming to improve the generalization of Graph Neural Networks (GNNs) on...
Journal of Intelligent Information Systems, Springer US, 29 novembre 2012, Volume : November 2012
Multirelational classification aims to discover patterns across multiple interlinked tables (relations) in a relational database. In many large organizations,...
37th Conference on Uncertainty in Artificial Intelligence, Association For Uncertainty in Artificial Intelligence (AUAI), 27 juillet 2021
Leveraging the framework of Optimal Transport, we introduce a new family of generative autoencoders with a learnable prior, called Symmetric Wasserstein...
Label Smoothing (LS) improves model generalization through penalizing models from generating overconfident output distributions. For each training sample the...
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