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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2402.18164 |
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| _version_ | 1866913677260619776 |
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| author | Bertrand, Jan Henrik Hoffmann, David B. Gargano, Jacopo Pio Mombaerts, Laurent Taws, Jonathan |
| author_facet | Bertrand, Jan Henrik Hoffmann, David B. Gargano, Jacopo Pio Mombaerts, Laurent Taws, Jonathan |
| contents | Recent advances in representation learning have successfully leveraged the underlying domain-specific structure of data across various fields. However, representing diverse and complex entities stored in tabular format within a latent space remains challenging. In this paper, we introduce DEEPCAE, a novel method for calculating the regularization term for multi-layer contractive autoencoders (CAEs). Additionally, we formalize a general-purpose entity embedding framework and use it to empirically show that DEEPCAE outperforms all other tested autoencoder variants in both reconstruction performance and downstream prediction performance. Notably, when compared to a stacked CAE across 13 datasets, DEEPCAE achieves a 34% improvement in reconstruction error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18164 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autoencoder-based General Purpose Representation Learning for Customer Embedding Bertrand, Jan Henrik Hoffmann, David B. Gargano, Jacopo Pio Mombaerts, Laurent Taws, Jonathan Machine Learning Artificial Intelligence 68T-02 Recent advances in representation learning have successfully leveraged the underlying domain-specific structure of data across various fields. However, representing diverse and complex entities stored in tabular format within a latent space remains challenging. In this paper, we introduce DEEPCAE, a novel method for calculating the regularization term for multi-layer contractive autoencoders (CAEs). Additionally, we formalize a general-purpose entity embedding framework and use it to empirically show that DEEPCAE outperforms all other tested autoencoder variants in both reconstruction performance and downstream prediction performance. Notably, when compared to a stacked CAE across 13 datasets, DEEPCAE achieves a 34% improvement in reconstruction error. |
| title | Autoencoder-based General Purpose Representation Learning for Customer Embedding |
| topic | Machine Learning Artificial Intelligence 68T-02 |
| url | https://arxiv.org/abs/2402.18164 |