Modèles connexionnistes de l'apprentissage

By Y. L. Cun, Françoise Fogelman-Soulié. 1987

Subfields: Model Architectures, Statistical Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Model Architectures

1. Max Welling
2. Yoshua Bengio
3. Timothy P. Lillicrap
4. Zhuowen Tu
5. Adam Santoro
6. J. Urgen Schmidhuber
7. Geoffrey E. Hinton
8. Blake Aaron Richards

In Statistical Learning Theory

1. Zhuowen Tu
2. Max Welling
3. Geoffrey E. Hinton
4. Olivier Colliot
5. Timothy P. Lillicrap
6. Adam Santoro
7. L. V. D. van der Maaten
8. Francis R. Bach

Citations in the corpus

• Dual Contradistinctive Generative Autoencoder (2020, neutral): “Autoencoder (AE) [42, 26] aims to compactly represent and faithfully reproduce the original input signal by concatenating an encoder and a decoder in an end-to-end learning framework.”
• Guided Variational Autoencoder for Disentanglement Learning (2020, background): “The resurgence of autoencoders (AE) [34, 6, 21] is an important component in the rapid development of modern deep learning [17].”
• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “In that case, the most common approach is to use an AE (Yann, 1987).”
• Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures (2018, background): “Perhaps the most practical among this family of “activity propagation” algorithms is target propagation (TP) and its variants [19, 20, 13, 3, 21].”